Nikitas-Rigas Kalogeropoulos
email: kalogeropo@ceid.upatras.gr

Ioannis Doukas
email: doukas@ceid.upatras.gr

Christos Makris
email: makri@ceid.upatras.gr

Andreas Kanavos
email: kanavos@ceid.upatras.gr

A Graph-based Extension for the Set-based Model Implementing Algorithms based on Important Nodes

Keywords: Information Retrieval, Retrieval Models, Set-based Model, Important Nodes, K-core Decomposition, Graph Degeneracy

The purpose of this paper is the expansion of the set-based model, namely an information retrieval model, with the use of graphs. The indexing process implements a graphical representation, while the querying and document representation are based on the classical setbased model. The root of the set-based model corresponds to the use of term sets to complete the querying process based on the terms of the query. However, in the weighting process, this paper presents a wholly different approach elaborating on algorithms that may clearly benefit the process based on the k-core decomposition of each single graph. The main focus will finally be the estimation and presentation of the most important nodes belonging to each graph. These intend to be regarded as keywords presenting the evaluation of their major influence.

Introduction

In Information Retrieval (IR), two of the main research subjects involve the creation of an effective and efficient retrieval model as well as the detection of keywords in textual data. In this study, following the work presented in [START_REF] Tixier | A graph degeneracy-based approach to keyword extraction[END_REF], we make an effort to combine those subjects, thus creating a new model by implementing a graphical representation of texts using keywords as important nodes, which are boosted at the weighting process. The case of the study is focused on whether the keywords can actually improve the retrieval process or not. The main difference among the proposed methods lies in the algorithms of discovering important nodes.

The set-based model is responsible for the retrieval process whereas, it is considered a combination of a set-theoretic and algebraic model. The algebraic notion of the model is expressed by the similarity function, which resembles the vector space model. More to the point, given a query, the model creates clustered sets of terms by utilizing the query terms. The termsets are then formed as the apriori algorithm proposes [START_REF] Agrawal | Fast algorithms for mining association rules in large databases[END_REF].

A well-structured graphical IR model is a rare subject of study. The main onus of the study is aimed at demonstrating either the graphical representation or the scoring functions. However, authors in [START_REF] Rousseau | Graph-of-word and TW-IDF: new approach to ad hoc IR[END_REF] proposed a complete graphical model; their implementation consists of unweighted directed graphs for each text of the collection and thus, the flow of the text is captured. The nodes of the graph represent the unique terms of the text while the edges refer to the relationship of the corresponding nodes within a partition of the text, expressed by a fixed-sized sliding window. As scoring function for the model, their implementation was based on the TF-IDF and BM25 using successive normalizations. One main difference regarding their scoring function, was the hypothesis that the weight of a vertex (T W ), which corresponds to a unique text term, contains more information than the term frequency. Therefore, their function can be summarized as T W -IDF , challenging the term independence assumption.

To further consolidate our point, we should mention that the above graphof-words model was utilized in [START_REF] Tixier | A graph degeneracy-based approach to keyword extraction[END_REF] on an attempt to estimate the keywords of a given text using algorithms based on graph degeneracy [START_REF] Seidman | Network structure and minimum degree[END_REF]. There, a method known as k-core decomposition, which creates subgraph having specific structural properties, was proposed in order to study the cohesion on social networks. The k-core decomposition of a graph G is considered as a maximal connected subgraph consisting of nodes with a degree at least equal to k. Following the above definition, a subgraph has core equal to k only when the entity of its nodes has a degree of k or more. The k-core, where k is maximal, is often called MainCore; it is used as the starting point of the keyword finding study, which afterwards is expanded on the remaining levels of decomposition. At lower levels, the cores tend to be large, and the core cohesion rises at higher levels of decomposition. A well known algorithm for the purpose of finding the cores of a graph at O(m) complexity was optly introduced in [START_REF] Batagelj | An o(m) algorithm for cores decomposition of networks[END_REF].

The decomposition aspect of defining the important nodes of a graph can be considered as a novelty. Centrality measures such as degree, closeness, and betweenness centralities are constituted as the most common alternatives [START_REF] Freeman | A set of measures of centrality based on betweenness[END_REF][START_REF] Freeman | The development of social network analysis: A study in the sociology of science[END_REF][START_REF] Prountzos | Betweenness centrality: Algorithms and implementations[END_REF]. More specifically, the degree centrality is based on the node degree, thus it can not necessarily reflect the magnitude of its importance. On the other hand, the closeness and betweenness centralities depend on the calculation of geodesic paths, which are difficult to be computed in complex graphs, especially on complete ones. Authors in [START_REF] Litvak | Graph-based keyword extraction for single-document summarization[END_REF] have focused on keyword extraction using graphical representation on texts as they elaborated on supervised methods using node centrality as well as unsupervised methods exploiting the HITS algorithm [START_REF] Kleinberg | Authoritative sources in a hyperlinked environment[END_REF].

The information retrieval models consist of set theoretic, algebraic and probabilistic models, where the main emphasis is given on the set-based model [START_REF] Pôssas | Set-based model: A new approach for information retrieval[END_REF][START_REF] Pôssas | Set-based vector model: An efficient approach for correlation-based ranking[END_REF]. A model, based on set theory, implements algebraic notions on scoring functions and document-queries representation, which is derived from the vector space model [START_REF] Salton | A vector space model for automatic indexing[END_REF]. The set-based model considers sets of terms; these terms in turn consist of a given query, known as termsets. The sets are mined using the apriori algorithm [START_REF] Agrawal | Fast algorithms for mining association rules in large databases[END_REF] that is responsible for supporting a minimum frequency boundary for each termset. There are however some cases where the set-based model uses a proximity measure to assure that terms are close enough, so as to have a rational relationship.

As mentioned above, the set-based model represents queries and documents in a similar concept as the vector space model. The same applies to the scoring function, which calculates the similarity between a given query and the collection documents. The gist of our proposal is based on the logical assumption that every text consists of words that carry great importance, known as keywords. Therefore, the corresponding text can be graphically represented by important nodes. If we can initially detect them and, in following amplify their importance, expressed by a boost on their weight, the retrieval process will be augmented.

Graphical Extension of Set-based Model using Important Nodes

Each document is represented as an undirected weighted complete graph. When node importance is considered, then this complete graph is in need of further processing aiming at edge pruning; this procedure is essential due to the fact that the core decomposition has only one level and therefore the whole graph is considered as important [START_REF] Seidman | Network structure and minimum degree[END_REF]. In following, a collection sized graph is created by the union of the respective text graphs. That is the point where, depending on the method, the important nodes are boosted by a value equal to their "importance". Finally, with the use of graph theory, a weight is produced for each term, which is considered at the retrieval process by the set-based model.

Rational Path Graph

Each document graph is created with the assumption that every term is related likewise to the other terms, as this particular word has been included in the text. Every term of a given document is represented by a node in the graph and the relationship between two nodes as a weighted edge with weight W out . Moreover, each node has a self loop edge, that is an edge with an identical starting and ending node having weight W in . The graph construction process is simplified by taking into consideration the two following theorems.

Theorem 1. Given a node N i in document D j with term frequency of the corresponding term equal to tf i , the in-weight of the self edge (N i , N i ) is computed as

W in = tf i × (tf i + 1) 2 (1)
Theorem 2. Given the nodes N i , N j in document D j with term frequency of the corresponding terms equal to tf i and tf j respectively, the out-weight of the edge (N i , N j ) is computed as

W out = tf i × tf j , when N i = N j (2)
With the use of these two theorems, we propose an alternative algorithm of the rational path graph that can be cost efficient as the time complexity is O(n 2 ) + O(m 2 ), where n is the number of the text terms and m the number of unique terms encountered in the text, as presented in Algorithm 1. We have to take into account the fact that this algorithm depends also on the way the documents are stored and consists of two steps; the term counting process as well as the graph creation. If the counting process is eliminated by the pre-processing of the text then the time complexity is O(m 2 ).

Algorithm 1 Proposed Graph Construction Algorithm

n = A[0] 13: tf t = A[1] 14: if n / ∈ G then 15:
Add end for 22: end for Initially, the algorithm creates a tuple of unique term and its respective term frequency by counting each and every individual word in the concrete text. In following, for each unique term, a node is created and the in-edge as well as the out-edge with every neighbour is initialized, if such an edge does not exist. Finally, the respective edge weight using the aforementioned theorems is calculated.

n to G with W in = tf t ×(tf t +1

Edge Pruning

The proposed methods depend on the degree of each node. Concretely, due to the fact that the graph is complete, the degree of each node is equal to N -1, where N stands for the number of nodes that the graph consists of. The inedge is not taken into consideration and therefore, there is a need to delete edges with minimum information loss. At first glance, someone would propose methods related to the connectivity of the graph [START_REF] Zhou | Simplification of networks by edge pruning. In: Bisociative Knowledge Discovery -An Introduction to Concept[END_REF]. However, the completeness of the graphs does not allow to calculate paths between nodes in a realistic manner. The computational complexity of such an act is O(n!) [START_REF] Sedgewick | Algorithms in C, Part 5: Graph Algorithms[END_REF]. Thus, a more naive approach was decided to be used, where initially the average weight of all graph edges are calculated and in following, all edges, with less weight than the average edge multiplied by a percentage P, are removed. P value is experimentally defined and is different in each collection. After this process, the important node estimation can be implemented on each graph separately.

Important Nodes Detection

The proposed methods focus on discovering the important nodes of a graph while simultaneously based on this graph's k-core decomposition. The decomposition of a graph is used for detecting influential nodes, where, in their absence, the graph might lose its connectivity. Authors in [START_REF] Seidman | Network structure and minimum degree[END_REF] suggested that given a graph G(V, E), a subgraph S(V , E ) is considered as the k-core decomposition of G if the degree of each node in V is less than k, considering that V ⊇ V and E ⊇ E. For example, the following Figure 1 presents the decomposition of a random graph G; the respective cores start with light grey nodes moving on to darker and finally to red ones. It is important to note that the k core, where the k +1 level of decomposition does not exist, is called main core.

Main

Core Initially, we consider that the main core includes all the key nodes of the graph similarly to [START_REF] Rousseau | Main core retention on graph-of-words for singledocument keyword extraction[END_REF]. The edges regarding those nodes have their weight amplified in the union graph.

Density Method

Following the logical process of [START_REF] Tixier | A graph degeneracy-based approach to keyword extraction[END_REF], i.e. the assumption of existence of all key nodes in the main core is too simplistic, we attempt to estimate the density of each core level of the text graph and consider as key nodes the nodes of the most dense subgraph. The densest subgraph was chosen by using the elbow method and the density of a subgraph S(V , E ) is computed as

density(S) = |E | |V | × (|V | -1) (3)

CoreRank Method

Finally, the scoring method [START_REF] Tixier | A graph degeneracy-based approach to keyword extraction[END_REF] is implemented into our model. CoreRank attempts to sort the nodes by the sum of the core number in which every neighbour exists. Thereafter, we assume that the important nodes should be chosen on document size basis and thus, the first 30% of the sorted node list are considered as important.

Union Graph and Graphs Weights

Ultimately, a collection size graph is created as the union of each text graph. Its nodes represent all the terms of the collection and the respective edges consist of the edges of each graph. The union graph edge weight is calculated as the sum of the weight in each graph, in which it exists. The proposed union graph algorithm updates the current union graph U with the graph G as presented in Algorithm 2. Initially, the union graph U is an empty graph, while the proposed algorithm checks for each term whether it constitutes an important node and determines the value of importance h. At this point, in case the model does not consider the node importance, then the important node list will be empty and therefore the value h will be equal to 1. In following, it checks if the term exists or not in the union graph. In case the condition is true, the W in weight of the term node in union graph is updated by the sum of the respective W in in graph multiplied by h (line 11). Subsequently, the update of the value of the out-edge weight for each node of the union graph takes place (lines 13 -14). In the absence of an edge between the term and the union graph node, one is initialized, whereas if the term does not exist in the union graph, the node is initialized and the update process begins.

When the union graph construction process is completed, a new weight N W for each node k emerges, derived from the union graph.

N W k = log 1 + a × W out k (W in k + 1) × (ng k + 1) × log 1 + b × 1 ng k + 1 (4)
where W out k is the sum of out-edges and W in k stands for the weight sum of all in-edges terms for a node k. The number of neighbours that corresponds to that particular node is expressed as ng k . Finally, a and b correspond to the gravity of the under review model against the set-based model.

W in (t, U )+ = W in (t, G) × h 12:
for all n ∈ G where n = t do

Add t ∈ U with W in (n, t, U ) = W in (n, t, G) × h 21:
Repeat lines 12 to 18 for this case 22:

end if 23: end for

Documents and Query Representation

The retrieval process is handled by the set-based model. As mentioned, the introduced termset notion contributes to the reduction of computational complexity due to the fact that the volume of processed data is significantly lower [START_REF] Pôssas | Set-based vector model: An efficient approach for correlation-based ranking[END_REF]. Termsets are created with the use of association rule mining algorithms [START_REF] Agrawal | Fast algorithms for mining association rules in large databases[END_REF] and the gist of the process is that we combine two termsets, different in only one element of the set, in order to produce a new termset expanded by one new term. The sets should have elements greater than a lower frequency bound, thus decreasing the complexity even more.

Weighting Process

At this point, the need of combining the derived graph weight with the set-based model's weight appears. For each termset S i , we calculate a new measure T N Si as the product of the graph weights of its terms.

T N Si = k∈Si N W k (5)
Thereafter, we include the new measure at the termset weight formula of a termset S i in document D j , where Sf ij represents the termset frequency (TF) and N dSi the inverse document frequency (IDF) of the corresponding termset

W Sij = (1 + log Sf ij ) × log 1 + N dS i × tnw Si (6)
The query scoring function is more simplistic as it is expressed by the inverse document frequency of the termset S i

W Siq = log 1 + N dS i (7)

Document Representation and Similarity

Finally, the document and query are represented as vectors in a similar manner with the vector space model. However, the vector space model uses T F/IDF as weight, instead of the above mentioned termset weighting process. Each vector d j is associated with a text document and contains the weights of every termset in that document. Because the termset is based on the query terms, it is obvious that the size of the vector would be no more than 2 n , where n is the number of words that the query contains.

- → d j = (W S1j , W S2j , • • • , W S 2 n j ) - → Q = (W S1q , W S2q , • • • , W S 2 n q ) (8)
As the set-based model dictates, the similarity between a document of the collection and the query is calculated using the cosine similarity. The result of this process is used for the ranking function of each model.

sim(Q, d j ) = - → d j × - → Q - → d j × - → Q (9)
The issue at hand in this case is that the calculation of the -→ d j is extremely difficult due to the high number of frequent termsets generated by the model, aggravated especially in large documents. Therefore, following the approach of the original set-based model, we should only consider the sets, which contain only one element as an estimation of the abovementioned norm; however, in our case, due to the size of the collection, we were able to compute the norm in a reasonable time.

Results and Discussion

The Cystic Fibrosis (CF) Database [START_REF] Shaw | The cystic fibrosis database: Content and research opportunities[END_REF] was used for the experimental evaluation, where all the included queries and documents were utilized. The number of queries and documents was 100 and 1238, respectively. We have initially examined whether the query size affected the retrieval process by using a subset of queries where the size of each one was less than 15. In following, all the proposed models as well as the set-based model were utilized as a basis of comparison. For each given query, the average precision of each model was calculated and afterwards, was compared with the average precision of the specific query of the set-based model. This specific comparison was expressed by the difference of the corresponding average precision values. In this way, we can determine if the under review model outperforms the set-based model.

Furthermore, the performance of each model against the set-based model in the set of queries can be quantified; this can be implemented by counting the number of queries where the model has greater average precision. It is important to note that a query is disregarded in case of equal results in that specific query between the models. Specifically, we have made evaluations with different values of importance variable h as well as pruning percentage value P in more than 84 experimental combinations.

For starters, the focus of study was to determine the performance of the important nodes amplification on the in-edges as well as out-edges of the main core model (M ainCore) in comparison with the simple graphical extension of the set-based model (GSB). It seems necessary to amplify the weights of both type of edges since the results were better in that case as presented in Figure 2. In following, we examined the performance of all 4 models, namely the simple graphical extension of the set-based model (GSB), the graphical extension of the set-based model using main core's nodes as important nodes (M ainCore), the graphical extension of the set-based model utilizing the most dense subgraph nodes as important nodes (Density) and the graphical extension of the set-based model employing CoreRank function (CoreRank) to determine the important nodes on 4 types of pre-processed textual data as depicted in Table 1. All the relevant information for each document has been initially included while this information was gradually pre-processed. Therefore 4 clusters of results are cre-ated, having different P values in each case. We can observe that in instances with low pre-process, the MainCore and the CoreRank models perform close to the set-based model, which is less affected by the noisy data. Emphasizing on the full pre-processed case, which only consists of the document title and text, we can observe in Figure 3 that our proposed models surpass the set-based model. At the best case, both GSB and M ainCore achieved a score of 60, 2%, having statistical significance p-value equal to 0, 02%. The Density model tends to be similar to the M ainCore model due to the fact that the majority of graphs have low decomposition levels. However, they differentiate at cases, which the results are not so impressive.

In addition, we examined the impact of the significance variable h in Figure 4. When the percentage of pruning is equal to 30%, the GSB, M ainCore and Density models tend to present similar results. On the other hand, the coreRank model comes last because of the need for higher pruning percentage value. Until this point, our aim was primarily focused on the number of queries that proposed models, which are actually superior to the set-based model disregarding the magnitude of that difference. In Figure 5, all results are sorted in ascending order based on the set-based model, thus allowing to elaborate on the difference in average precision on each query. We choose to depict an average case and a case where M ainCore, Density and CoreRank fall behind GSB. Nonetheless, the proposed methods perform similarly in some occurrences. Because of the fact that the graph is complete, as we assumed during the graph creation process, the pruned percentage variable seems to be more important than the significance variable; that precisely should be the motive for further research. One alternative is to relate each term of the document in sentence or paragraph level implemented by a moving window or alternatively utilize a proximity penalty at the edge weighting process.

"Western Greece 2014-2020", under the Call "Regional Research and Innovation Strategies for Smart Specialisation -RIS3 in Information and Communication Technologies" (project: 5038701 entitled "Reviews Manager: Hotel Reviews Intelligent Impact Assessment Platform").

Fig. 1 .

 1 Fig. 1. K-core Decomposition of a Graph

Fig. 2 .

 2 Fig. 2. Amplification on Important Nodes-Edges

Fig. 3 . 4 .

 34 Fig. 3. Full Pre-processed Case Fig. 4. Each Significance Variable Case

Fig. 5 .

 5 Fig. 5. Difference with Set-based Model

Fig. 6 .

 6 Fig. 6. Average Precision of each Model

  1: input Document D j 2: output Graph G of Document D j 3: Given array A, list L, term t, node n, neighbour ng

	6:	if t / ∈ A then
	7:	tf = L.count(term)
	8:	A = (term, tf )
	9:	end if
	10: end for

4: Insert each t in L 5: for all t ∈ L do 11: for t ∈ A do 12:

  Algorithm 2 Proposed Union Graph Construction Algorithm 1: input Graph G, Union Graph U 2: output Updated Union Graph including Graph U G 3: Given term t, node n, k-core list kc, value of importance h 4: for all t ∈ G do

	5:	if t ∈ kc then
	6:	Gain value of importance
	7:	else
	8:	h = 1
	9:	end if
	10:	if t ∈ U then
	11:

Table 1 .

 1 Proposed Models Evaluation for different Pre-processed Cases

	Pre-processed Case Size of Index Result Model
	Low	48452	49	MainCore
	None	58621	41.17 CoreRank
	Slight	36988	47.42 MainCore
	Full	11366	60.20 MainCore, GSB

Acknowledgement

Christos Makris and Andreas Kanavos have been co-financed by the European Union and Greek national funds through the Regional Operational Program


Babu Lakshmi
email: lakshmi.babu-saheer@aru.ac.uk

Mohamed Saheer
email: mohamed.shahawy@student.aru.ac.uk

Javad Shahawy

Zarrin

Mining and Analysis of Air Quality Data to Aid Climate Change

Keywords: Data Mining and Analysis, Data Pre-Processing, Air Quality, Climate Change, Urban Planning and Machine Learning., Geographic Information Systems

The data science and AI community has gathered around the world to support tackling the climate change problem in different domains. This research aims to work on the air quality through emissions and pollutant concentration data along with vegetation information. Authorities especially in urban cities like London have been very vigilant in monitoring these different aspects of air quality and reliable sources of big data are available in this domain. This study aims to mine and collate this information spread all over the place in different formats into usable knowledge base on which further data analysis and powerful Machine Learning approaches can be built to extract strong evidences useful in building better policies around climate change.

Introduction

Climate change is the main challenge that humanity is facing today, threatening the existence of life on earth. Awareness campaigns and drastic steps towards bringing the situation under control have been initiated in every nook and corner of the world. Both developed and under developed countries are working hard to tackle this problem. Data Analysis, Data Science and Artificial Intelligence have big potential to help mankind where ever (big) data is available to help build models and make predictions or provide prescriptive solutions. Such models for emissions, resources, energy consumption, etc., have already been statistically worked out by specialist groups around the world to tackle climate change [10]. ClimateChangeAI [START_REF]Climate Change AI[END_REF] is a classic example of such an initiative.

Motivation

There are sources of big data available in the fields of energy consumption, transport, building & cities, carbon footprint, farming, climate change and prediction etc( [START_REF] Rolnick | Tackling climate change with machine learning[END_REF]). It is really hard to mine all this data in a reasonable time to get useful resources with mathematical models. The approach for our research would be to first identify the most useful and informative data for the identified topics of climate change and start to build machine learning models and methodologies to extract useful and relevant information in the form of predictions or prescriptions. Even though a lot of data is available for every field, it is very difficult to gather the majority of these information in a structured useful format.

Research Objective

The research presented in this work is to look at the parallel data on pollutant concentrations, road traffic emissions and vegetation specifically around city like London. "Parallel" in this scenario refers to different datasets collected in close timelines and physical locations. London is chosen as the city to look at mainly because there is a lot of publicly available data for this city from various sources. This can directly help us plan our cities and traffic routes or even come up with laws to keep our carbon footprint under control. Further, our hypothesis (evidenced by initial experiments and literature) is that one of the major factors affecting the concentration of pollutants in atmosphere is the vegetation. This research aims to find traffic and air quality or emissions monitoring datasets along with the information of vegetation around the area to analyse the importance and effectiveness of vegetation in urban planning.

Background

There have been several studies to understand the potential benefits of vegetation in mitigating urban air pollution problems ( [START_REF] Monks | Air quality expert group: Impacts of vegetation on urban air pollution[END_REF], [START_REF] Benjamin | Estimating the ozone-forming potential of urban trees and shrubs[END_REF], [START_REF] Fares | Particle deposition in a peri-urban Mediterranean forest[END_REF]). Bealey et.al. [START_REF] Bealey | Estimating the reduction of urban PM10 concentrations by trees within an environmental information system for planners[END_REF] states that trees have been widely quoted as effective scavengers of both gaseous and particulate pollutants from the atmosphere. Further, effect of tree planting strategies on dispersion and deposition of airborne urban aerosol concentrations onto woodland is suggested to be considered in the planning process. Several studies have suggested the importance of including the vegetation in urban road planning ([1], [START_REF] Baldauf | Integrating vegetation and green infrastructure into sustainable transportation planning[END_REF]). It is even suggested that vegetation in urban settings can provide benefits beyond improvements in air quality-these include carbon sequestration, temperature and storm water regulation, noise reduction, aesthetic improvements, and opportunities for physical exercise and the experience of nature.

Most of these research use expert knowledge and postulations to predict these impacts. In this modern age, scientists should work on big data and deductions from the proofs evidenced from data. To this end, this research will collate data from different sources dispersed all over internet regarding emissions (like CO 2 , N O x , P M 2.5 , P M 10 ), concentration of gaseous and particulate matter (like N O 2 , N O x , P M 2.5 , P M 10 ), road transport, and vegetation details into useful parallel information base. This pool of information could help us model different Machine learning systems to understand the relations between and impact of each of these factors on the overall quality of air. The main aim of this research is finding the links between vegetation, transport and other factors on pollutant concentration. This should help us validate the earlier suggestions on urban vegetation planning. Further, this project should help in building a proposal system to the local authorities on the type and form of vegetation to be planted around our cities to help control the effects of emissions.

There has been other efforts in the area of data analysis of air quality using advanced Machine Learning techniques for data imputation [START_REF] Junninena | Methods for imputation of missing values in air quality data sets[END_REF]. Junninena et. al. [START_REF] Junninena | Methods for imputation of missing values in air quality data sets[END_REF] looks at only recreating missing pollutant values in a specific air quality dataset. The other studies mentioned earlier ( [START_REF] Monks | Air quality expert group: Impacts of vegetation on urban air pollution[END_REF], [START_REF] Benjamin | Estimating the ozone-forming potential of urban trees and shrubs[END_REF], [START_REF] Fares | Particle deposition in a peri-urban Mediterranean forest[END_REF], [START_REF] Bealey | Estimating the reduction of urban PM10 concentrations by trees within an environmental information system for planners[END_REF]) approach the problem as a data collection exercise as a validation of their theoretical hypothesis. It seems like our research is the first systematic effort combining the different aspects like emissions, concentration and vegetation by collating existing big data collected from different sources to perform a detailed data analysis. This in turn leads to several challenges in data pre-processing as discussed in Section 3.

Data Mining Approach

The first steps in this work is to identify parallel data from different sources for road transport information including number and type of vehicles, emissions in the air with different types of pollutants, pollutant concentrations, vegetation type and extend. Parallel in this scenario, as mentioned earlier, refers to both same geographical location and similar or same time scales. The idea is to build such an open source parallel database with different levels of geographic and timescale granularity. The geographic information could be as coarse as starting from big cities, to different regions in a city (boroughs), to finally narrowing down to road level details. Similarly, time scales could vary from seasons (especially where the difference would be evident with leaf shedding in trees) to peak traffic timings in the day. Time scale granularity is left as a future exercise due to unavailability of reliable resources. For the sake of simplicity, the location granularity is also taken to be at a region or borough level. Hence, the task is to find the parallel data for air pollutants in terms of concentration and emission along with data on vegetation at borough level. Later on, this information could be made more granular in terms of location and time.

This huge knowledge base once ready is expected to provide a wealth of information for the research community to study the interactions between these factors and how these learning can be combined to form useful environmental policies. Some of the main challenges in this work is mainly around finding parallel compatible information including the formats of the information is discussed in detail in the next section. The main hypothesis in this work is that Vegetation has a positive effect in reducing the concentration of the pollutants despite the emission rates. The aim of this research is to prove if the data available can support this hypothesis.

Research Challenges

As mentioned earlier, the UK city of London is chosen as the first city to target our study merely due to the availability of good quality data from various reliable sources. For this particular study, different public domain datasets available through government and public authorities are considered. The traffic monitoring data in UK is available with different authorities like: Cambridgeshire county council: ( The challenge would be to map these different sources to come up with parallel data to extract useful information through machine learning approaches like SVMs or Neural Networks ( [START_REF] Mundhenk | A large contextual dataset for classification, detection and counting of cars with deep learning[END_REF], [START_REF] Gastaldi | Annual average daily traffic estimation from seasonal traffic counts[END_REF], [START_REF] Krile | Assessing roadway traffic count duration and frequency impacts on annual average daily traffic estimation: Assessing accuracy issues related to annual factoring[END_REF]), [START_REF] Tsapakis | Use of support vector machines to assign shortterm counts to seasonal adjustment factor groups[END_REF]). Traffic for London seems to have usable parallel data for traffic and emissions and is known to publicly share this information to support research. This again validates London to be a good starting point for this research, later on extending to other cities or more resources for further information.

There are several challenges even with these available datasets for London. The data available is only partially aligned with varied features between the measured concentrations and emissions. The table 1 clearly shows the misalignment between the concentrations or emissions features, which limits the correlation analysis and the amount of useful information that could be inferred from the data. Next section presents how some of these challenges are dealt with in this study in order to perform further data analysis.

Technical Challenges

The London Atmospheric Emissions [17]) dataset, collected by the Greater London (GLA) and Transport for London ([24]), is chosen as the primary source of data in the following analysis. While the dataset provides a vital and invaluable source of geographic information, it also has its drawbacks regarding formatting and data-types. The varied types of geographic references (as shown in the table 1) pose an issue with both the granularity of the entire analysis (limited to London boroughs at the highest-level), as well as finding means of converting the reference types for uniformity purposes.

The first challenge was to find the data related to each borough in London. Due to the lack of support for Ordinance Survey National Grid coordinates converters, especially all-numeric grid references, a supplementary OSGB36 or WGS84 python library (PyBNG) was developed and open-sourced on GitHub and PyPi ( [23]) as an outcome of this research. Using PyBNG, OSGB36 coordinates were converted to latitude and longitude, which can be used to find the corresponding London boroughs. The large volume of the concentration dataset, In order to synthesize or validate the boroughs feature in some of the datasets mentioned, the ray-cast algorithm was used. This algorithm is used to determine whether or not the corresponding longitude & latitude coordinates are encapsulated within the boundaries of a geo-polygon of the corresponding London borough (Figure 1). Another challenge was to check for wrong or inconsistent data. Whilst verifying some of the trees dataset, it became apparent that the boroughs were incorrectly entered; the coordinates given for some rows do not correspond to the boroughs provided. To validate the entries, PyBNG was used to convert easting/northing to latitude and longitude coordinates, which were then used to infer and ultimately validate the borough for every entry. Another hurdle encountered was the varying invalid entries like the tree species names ("Failed Planting Site", "Unknown Conifer", "Zz Tree Missing", etc). Manually filtering the valid species names was a painstaking task given the fact that there are thousands of different species in the dataset. Alternative techniques for filtering using crawlers may be pursued in future to expand this data.

Sample output dataset after pre-processing and collation is shown in Table 2. Given that the data pre-processing and aggregation proved to be a challenging task, the finalized dataset will be released publicly in the near future to assist with other potential climate change solutions.

Data Analysis

The data-analysis process heavily relied on the aggregation of the datasets given the high volume of features. The goal was to extract the useful trends out of thousands of features whilst minimizing the loss of data and maintaining uniformity of geographic level. This was achieved by pre-processing subsets of data individually and guaranteeing uniformity prior to collating all the subsets together (essentially a divide-and-conquer data pre-processing approach). A more abstract level of the data was ensured in the end to be consistent.

The pollutants monitored for air quality can be categorised into two classes -gaseous (CO 2 , SO 2 , N O x , N O 2 ) and particulate matter (P M 10 , P M 2.5 ). It could be postulated that the trees help absorbing only gaseous pollutants. But, there have been reports on certain types or species of trees that could help absorb the particulate matter as well( [START_REF] Chen | Variation in tree species ability to capture and retain airborne fine particulate matter (PM2.5)[END_REF], [START_REF] Bealey | Estimating the reduction of urban PM10 concentrations by trees within an environmental information system for planners[END_REF]). Data analysis is performed separately on the effects on gaseous and particulate matter. Unfortunately, this study has not yet looked into the species of trees in the dataset. This may be included in the future extensions of this research. Again, the particulate matter may seem to have more effect on the general health rather than climate change. But, it is known that the particulate matter has fractions of elementary carbon ( [START_REF] Chernyshev | Morphological and chemical composition of particulate matter in buses exhaust[END_REF]) which results in global warming and hence, affecting climate change directly. These types of pollutants also need to be included in this study.

Another limitation with the current emissions and pollutant concentration datasets is that the type of pollutants monitored are not the same. The emissions data has CO 2 , N O x , P M 2.5 and P M 10 and the concentration data has N O x , N O 2 , P M 2.5 and P M 10 . It is unfortunate that there is only N O x , P M 2.5 and P M 10 that are aligned and could be studied in parallel. Efforts are underway to gather more information from the authorities on other pollutants like CO 2 monitored as pollutant concentration. At this stage of the research, focus is only on these 3 pollutants to study the direct relations between emissions and concentration. It has to be also noted that there is difference in metrics of representation for concentration as µg/m 3 and emissions as tonnes/year. This time granularity for the data is annual values and hence concentrations are aggregated to mean concentrations per year as well.

Once the parallel data for emissions, pollutant concentration and vegetation (as number of trees) at borough level is obtained, the next step is to look for correlations between these features. The correlations between these three features are first plotted as graphs and heatmap. A negative correlation is expected between the number of trees and pollutant concentration. The values in the graph should be representing each borough in London. A closer look at the number of trees against mean values of concentrations of gaseous and particulate matter distributions for each borough could be plotted separately. Finally, number of trees, emissions and concentrations should be looked at separately for the three common pollutants viz., N O x , P M 2.5 and P M 10 . Next section visualizes and analyzes these plots in detail.

Results and Discussion

It is quite evident even from the initial analysis that the existence of trees has a negative correlation with the mean concentrations of both gaseous and particulate matter as shown in figure 2. Figure 3 shows a heatmap of correlation between the emissions, concentration and trees. As expected, there is a negative correlation between trees and concentrations. Also, there is a negative correlation between emissions and concentration which could be the effect of trees. This could be further investigated by looking at the boroughs with lower tree counts.

Looking at each borough in detail, figure 5 shows the relation of number of trees with the particulate matter concentrations. Similarly, figure 4 shows the effect of trees on gaseous pollutants. It can be observed that most of the boroughs with a low volume of vegetation have relatively higher pollutant concentration means, while those with a higher volume of vegetation have low pollutant concentration. Each point or bar in the graph represents a borough. The difference in y-axis scales of these two graphs needs to be noted which suggests that there is lower variation in the particulate matter and more variation in the gaseous matter. There is no doubt that all types of trees would help in CO 2 absorption. Not all trees are particularly known to absorb nitrous oxide which could explain some discrepancies here, again, suggesting a deeper look into the species of the trees.

A statistical concern that arose was the potential collinearity (negative correlation) between the emissions and the number of trees per borough. The initial hypothesis can be contested by claiming that the concentrations measured are lower than expected due to low emissions in the area rather than it being the vegetation's effect. Separate plots for each of the three parallel pollutant in both datasets have been presented in figure 7. These figures further prove the hypothesis that concentration is low with even high emissions where there are more trees. It needs to be clarified that the emissions in the dataset includes only the transport or road emissions and hence in some cases, it is observed that concentrations are much higher than emissions. This again is a future work to look into. For the sake of sanity, if we look at boroughs with more than 20000 trees, it is seen that the there is a strong negative correlation between concentration and emissions as shown in figure 6. Hence the results of our data analysis supports and proves our hypothesis.

Conclusion

More and more climate data from different sources are being collected now than ever before. Analysing such big data can offer unprecedented opportunities to create innovative climate solutions and influence decision-making on a global level. This research initiates the study of influence of AI on tackling climate change with regards to air quality data. The various sources of big data that could contribute to a knowledge base facilitating a detailed data analysis (prior to applying Machine Learning algorithms) are being collated. The challenges faced during this process is briefly outlined to gauge the efforts needed in this research. The initial aim was to build borough level information for London city in order to perform data analysis. The initial analysis of this parallel data at borough level confirms the hypothesis that the vegetation is negatively correlated to the pollutant concentration. This motivates further detailed analysis on this topic to look at better granularity in time and location and impact of other factors like transport emissions, types of trees, weather etc.

Future Work

As mentioned in the earlier sections, there are a number of details to be figured out in the existing datasets extending the quality, quantity and granularity of information. This paves way for further data mining and data processing. Examples of these extensions include looking at tree species, more types of pollutants, more granularity of information in location and time, etc. Authorities like TFL will be contacted along with searching for other (online) resources. Finally, the research is intended to progress as a data gathering project to acquire good quality first hand information to implement the machine learning approaches derived from the initial studies.

To further eliminate any potential collinearity that might be biasing the measured concentrations and hence disproving the deductions made, other sources of emissions must be taken into account. Seeing as road transportation constitute roughly 25% to 35% of the total emissions in the Greater London region [17] and the concentrations of pollutants are measured regardless of the source, the total emissions produced should be considered.

Using AI for climate change control through efforts like the traffic-emissions control could be a major step forward to build upon strategies for tackling climate change based on big data available in various fields to build a sustainable future. Once this big data knowledge base is available, different data analysis techniques could be worked on to understand the correlations, Machine learning techniques like standard regression models (linear and non-linear) have already been used to predict the air quality ( [START_REF] Zhu | A machine learning approach for air quality prediction: Model regularization and optimization[END_REF]). Another review [START_REF] Rybarczyk | Machine learning approaches for outdoor air quality modelling: A systematic review[END_REF] shows that air quality estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting makes use of Neural Networks and Support Vector Machines. Since different factors are at play, it might be worth identifying correlated information using unsupervised clustering algorithms and later on use expert systems with some Bayesian prior probabilities to take into consideration several related factors.

Due to the multi-aspect nature of the problem and its internal dynamism, Multi-Perspective Machine Learning (MPML) and Classifier Ensemble can also be considered to investigate various parallel aspects and co-factors involved in the scenario. Furthermore, it is possible to enrich weak datasets through leveraging open source data and using automated approaches for harvesting and integration of publicly available online data.

  [5]), Highways England data:([13]) and Traffic for London: ([25]). Similarly, air quality datasets in the form of emissions and pollutant concentration monitoring data in London is available with authorities like: London Air: ([16]), UK Government Monitoring: ([17], [9]), Traffic for London: ([25]). Particularly for London, the local authorities have also kept track record of Vegetation information around London ([18]).

Fig. 1 .

 1 Fig. 1. Greater London Boroughs' Geo-Spatial Boundaries

Fig. 2 .

 2 Fig. 2. Trees-concentration Correlations Fig. 3. Correlation: emissions, concentration and Trees

Fig. 4 .

 4 Fig. 4. Trees: Effect on Mean gas ConcentrationsFig. 5. Trees: Effect on Mean PM Concentrations

Fig. 6 .

 6 Fig. 6. Trees>20000: Correlation of Emissions Vs Concentrations

Fig. 7 .

 7 Fig. 7. Comparison: emission with concentration and trees for N Ox, P M2.5 and P M10

Table 1 .

 1 Dataset format details

	Data	File-Type Geographic Reference	Available Data	Unit
	Transportation	XLSB (multi-sheet)	London borough-level, ArcGIS project main motorways,	Emissions / vehicle type Tonnes/year
	Concentration	CSV	All-numberic, OSGB36 coordinates	N O2, N Ox, P M10 and P M2.5	µ g/m3
	Emission	XLSB (multi-sheet)	London borough-level, ArcGIS project	CO2, N Ox, P M10 and P M2.5	Tonnes/year
			London borough-level,
	Trees	CSV	OSGB36 coordinates,	Species name	N/A
			WGS84 coordinates

Table 2 .

 2 Processed dataset sample

	Borough Tree Count Road Emissions (tonnes/yr) Concentrations (µg/m 3 )
	Names Numbers	CO2	N Ox P M2.5 P M10 N O2 N Ox P M2.5 P M10
	City	1361	52817.85 220.46 9.33 15.57 54.04 126.96 16.02 27.41
	Ealing	42082	285274.47 934.35 51.08 95.17 36.35 65.51 13.17 21.79
	...	...	...	...	...	...	...	...	...


Georgios Drakopoulos

Ioanna Giannoukou

Mylonas
email: fmylonas@ionio.gr

Spyros Sioutas
email: sioutas@ceid.upatras.gr

The Converging Triangle Of Cultural Content, Cognitive Science, and Behavioral Economics

Keywords: Cognitive science, Behavioral economics, Cultural content, Content delivery, Graph recommendation, Graph databases, Graph analytics, Neo4j, Cypher, Humanistic data

How online cultural content is chosen based on conscious or subconscious criteria is an central question across a broad spectrum of sciences and for the entertainment industry, including content providers and distributors. To this end, a number of tailored analytics forming the backbone of recommendation engines specialized for retrieving cultural content are proposed. Their strength derives directly from wellestablished principles of cognitive science and behavioral economics, both scientific fields exploring aspects of human decision making. Another novel contribution of this conference paper is that these analytics are implemented in Neo4j expressed as Cypher queries. Various aspects of the cultural content and digital consumers can be naturally represented by appropriately configured vertices, whereas edges represent various connections indicating content delivery preferences. Early experiments conducted over a synthetic dataset mimicking the distributions of preferences and ratings of well-known movie datasets are encouraging as the proposed analytics outperformed the baseline of a multilayer feedforward neural network of various configurations. The synthetic dataset contains enriched preferences of mobile digital consumers of cultural content regarding literature of the Greek region of Ionian Islands.

Introduction

Interpreting and predicting, within a reasonable degree of error, the selection of online cultural content or even a sequence of such selections, has been a central topic in various scientific fields ranging from economics and social science to artificial intelligence (AI) and machine learning (ML) as well as to interested parties such as platform managers and content providers.

During the current decade the creation cultural content has reached unprecedented levels in terms of number of generators, languages, modalities, and topics. Given the ease of access to equipment ranging from an off the shelf smartphone to a digital camera as well as the prevalent culture encouraging creativity and originality, a considerable number of independent content contributors has been added to established entities such as cultural foundations, academic institutions, government agencies, and private organizations.

The primary research objective of this conference paper is to lay the groundwork for graph analytics based on sound principles of both behavioral economics and cognitive science and tailored for the recommendation of personalized delivery of cultural content. Recommendations not only assist in navigating among the plethora of available digital cultural content, but also help the digital consumer discover new and potentially interesting material. The contribution of this work is that augments the few cultural analytics, especially those implemented over a graph database, whereas giving new directions to them.

The principal motivation besides this work is that the delivery of cultural content requires specific conditions which differentiate it from ordinary digital entertainment. Specifically, cultural, educational, and location parameters must be taken into consideration.

The remaining of this work is structured as follows. Section 2 briefly reviews scientific literature regarding recommendation engines, behavioral economics, and cognitive science. The architecture and the queries of the proposed recommendation system as well as the intuition behind them are given in 3. The results of the experiments conducted so far with the proposed analytics and the baseline neural networks (NNs) are presented in section 4. Finally, the conclusions and the directions for future work are given in 5. Tensors are represented with capital calligraphic letters, whereas capital and small boldface are reserved for matrices and vectors respectively. Table 1 summarizes the notation of this work.

Previous Work

Scientific literature abounds with works regarding general-and special purpose recommendation systems for various tasks relying on diverse technologies. Then, a shift towards adaptive and content-based recommendation systems was made as is made clear from works like [START_REF] Pazzani | Content-based recommendation systems[END_REF] and [START_REF] Lops | Content-based recommender systems: State of the art and trends[END_REF]. Initializing a recommendation system and associate issues are explored in [START_REF] Lam | Addressing cold-start problem in recommendation systems[END_REF]. Recently, with the advent of Semantic Web, incorporating trust into recommender systems has been also added to the line of research [START_REF] Andersen | Trust-based recommendation systems: An axiomatic approach[END_REF]. Applications of recommendation systems include software engineering as discussed in [START_REF] Robillard | Recommendation systems for software engineering[END_REF].

Cultural analytics are the offshoot of social sciences and data science aiming at providing deeper insight into the individual and collective cultural background [START_REF] Manovich | Cultural analytics: Visualising cultural patterns in the era of more media[END_REF] and [START_REF] Manovich | How to follow global digital cultures, or cultural analytics for beginners[END_REF]. Possible transitions to data-driven cultural analytics are discussed among others in [START_REF] Hall | Toward a postdigital humanities: Cultural analytics and the computational turn to data-driven scholarship[END_REF], whereas applications include tracing social patterns in social media [START_REF] Salah | Combining cultural analytics and networks analysis: Studying a social network site with user-generated content[END_REF] such as Instagram [START_REF] Hochman | Visualizing Instagram: Tracing cultural visual rhythms[END_REF] and flickr [START_REF] Ushizima | Cultural analytics of large datasets from flickr[END_REF].

Behavioral economics is a field studying human behavior from a decisionmaking point of view [START_REF] Camerer | Behavioral economics: Past, present, future[END_REF]. Recurring major topics include conflict and compliance [START_REF] Schmid | Conflict and cooperation: Institutional and behavioral economics[END_REF], corporate management [START_REF] Kremer | Behavioral development economics[END_REF], public policy [START_REF] Chetty | Behavioral economics and public policy: A pragmatic perspective[END_REF], rewards and gamification [START_REF] Bernheim | Handbook of Behavioral Economics: Foundations and Applications[END_REF], and learning to evaluate rational choices with a multitude of criteria [START_REF] Diamond | Introduction to behavioral economics and its applications[END_REF]. The psychological grounds of the field are given in [START_REF] Kahneman | Maps of bounded rationality: Psychology for behavioral economics[END_REF], while a recent overview of the field is given in [START_REF] Wilkinson | An introduction to behavioral economics[END_REF].

Cognitive science covers the study of human mental activity at various levels [START_REF] Stillings | Cognitive science: An introduction[END_REF]. Sources include high level psychological and sociological observations, education, as well as low level data about brain physiology and connectivity [START_REF] Clark | Mindware: An introduction to the philosophy of cognitive science[END_REF]. A framework for evaluating decision making is presented in [START_REF] Fellows | The cognitive neuroscience of human decision making: A review and conceptual framework[END_REF] and mental decision models in [START_REF] Yechiam | Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits[END_REF]. A thorough revision of human decision making from a cognitive science perspective is given in [START_REF] Busemeyer | Cognitive science contributions to decision science[END_REF]. The relationships between brain functionality and the structure of the human nervous system are explored in [START_REF] Varela | The embodied mind: Cognitive science and human experience[END_REF], while the connections between cognitive sciences and language are investigated in [START_REF] Bloch | Language, anthropology, and cognitive science[END_REF].

Tensor algebra is the generalization of matrix algebra to three or more dimensions [START_REF] Kolda | Tensor decompositions and applications[END_REF]. Traditionally, tensors were widely applied to singal processing applications such as brain MRIs [START_REF] Westin | Processing and visualization for diffusion tensor MRI[END_REF], MIMO radars [START_REF] Nion | Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar[END_REF], multispectral imaging [START_REF] Peng | Decomposable nonlocal tensor dictionary learning for multispectral image denoising[END_REF], and face recognition [START_REF] Vasilescu | Multilinear analysis of image ensembles: Tensorfaces[END_REF]. However, recently tensor analytics have been implemented over Neo4j in order to combine structural and functional higher order analytics in [START_REF] Drakopoulos | Defining and evaluating Twitter influence metrics: A higher order approach in Neo4j[END_REF]. Moreover, a genetic algorithm for approximately clustering a tensor with spatiosocial data has been implemented in [START_REF] Drakopoulos | A genetic algorithm for spatiosocial tensor clustering: Exploiting TensorFlow potential[END_REF].

Analytics

Implementation

Figure 1 depicts the overview of analytics architecture. In essence, the architecture is a long feedback loop where digital consumers are given suggestions as to which piece of cultural content to select based on their past decisions. Neo4j is a graph database where graphs are natural, namely they are physically represented as a graph [START_REF] Drakopoulos | Tensor fusion of social structural and functional analytics over Neo4j[END_REF]. From an implementation perspective, a data structure which is efficient and versatile enough to store an implementation of the property graph, the conceptual model of Neo4j, is described in [START_REF] Kontopoulos | A space efficient scheme for graph representation[END_REF]. The Neo4j instance was interfacing with a Python client running the py2neo module, which allows both the formulation of dynamic Cypher queries and the delcarative manipulation of the database.

Analytics

Analytics Engine Choices

Consumer

Content

Rec.

Fig. 1. Proposed analytics architecture.

Ties to behavioral economics and cognitive science

The recommendation architecture is explicitly or implicitly based on findings from behavioral economics and cognitive science. Also, it should be noted that the names of these findings are not unique as various studies may give them different names. The first finding establishes that a digital consumer is very likely to follow the recommendations. This is part justifies the frequent selections.

Proposition 1 (Default options).

A digital consumer is more likely to follow certain default options rather than change them.

The second finding establishes that properly designed recommendations are likely to be relevant.

Proposition 2 (Sliding window).

There is a limited selection window where the preferences of the digital consumer remain constant.

The third finding justifies the inclusion of past selections to the recommedations list as well as suggestions which are similar to past selections.

Proposition 3 (Cognitive bias).

A consumer will tend to select content she/he agrees with.

Analytics

The first task is to define the entities, which in the graph database world are usually but not exclusively modeled as vertices, as well as the possible connections between them, which frequently form the edges. Here it should be noted that representing connections as vertices may sound counterintuitive, but within a graph database design context may well make sense.

For the purposes of this work, the following entities are considered:

-Digital consumers, which often are individuals but may well represent institution accounts, company accounts, or intelligent agents. Currently, they have the following properties: name, which is the unique vertex name, hist, which contains the last n content selections of the particular consumer, and kw, which is an array of at least three of the prespecified keywords of the right column of table 2. -Cultural content, which may well assume any form such as text, video, or music. Currently, they are composed of the following properties: id, which is the unique vertex id, and kw, which contains the keywords related to that verex.

How content simialrity can be quantified? A first approach is to compute the Tanimoto similarity coefficient between two content vertices and to insert a unique edge labeled SIMILAR with the actual coefficient value being a single edge property. Recall that said coefficient is defined as:

τ (S 1 , S 2 ) = |S 1 ∩ S 2 | |S 1 ∪ S 2 | = |S 1 ∩ S 2 | |S 1 | + |S 2 | -|S 1 ∩ S 2 | (1)
An alternative approch relies on semantics and mapping sets to distributions. Specifically, each aspect keyword in the list of a content vertex is connected to a fundamental content type, as shown in table 2. Consider, for instance, the following (fictional) keyword list: lyrics , music , opera , director , singer , actor , author

Given the entries of table 2, then the corresponding distribution is:

d = 4 7 , 1 7 , 2 7 (3)
The above distribution was derived by dividing the number of keywords from each category of table 2 (in the order they are shown) to the total number of keywords in the list. Also, observe that each keyword has been selected on purpose to represent a specific aspect of the content type. Thus, aspect-specific analytics can be constructed as well. Moreover, each keyword has been assigned to only one category in order to avoid ambiguity. Once two distributions are given, their proximity can be computed using the cosine similarity:

c (d 1 , d 2 ) = p i=1 d 1 [i] d 2 [i] ( p i=1 d 1 [i]) 1 2 ( p i=1 d 2 [i]) 1 2 (4)
In order to create a digital consumer vertex the following Cypher query should be used. Notice that every Cypher query must return a result, in this The following Cypher constraint ensures that the property name of a vertex of a type consumer is unique: create constraint on ( dc : consumer ) assert dc . name i s unique

Having ensured the uniqueness of each consumer, the next query returns a list of all consumers in the system: match ( dc : consumer ) return dc . name as name order by name Moreover, when a digital consumer accesses a certain piece of cultural content, then an ACCESS relationship is additionally created to indicate a generic access type with the hits property set to one. The merge clause is used as a safeguard against inserting multiple access relationships between the same pair of vertices. match ( dc : consumer {name : ' name ' } ) , ( c c : c o n t e n t { i d : ' i d ' } ) merge ( dc ) -[ r : ACCESS { h i t s : 1}]->( c c ) return r as edge On the contrary, when this is not the first time the consumer asks for this content, then the hits property can be updated like this: match ( dc : consumer ) -[ r : ACCESS]->( c c : c o n t e n t ) where dc . name= ' name ' and c c . i d= ' i d ' s e t r . h i t s = r . h i t s + 1 return r . h i t s as h i t s

To access all the distinct cultural content a specific consumer with a given name has ever accessed it suffices that the following query is issued:

match ( dc : consumer ) -[ :ACCESS]->( c c : c o n t e n t )
where dc . name = ' name ' return d i s t i n c t ( c c . i d )

In this case the label ACCESS is used as it is not important whether the consumer was interested on the content as a whole or focused on specific aspects thereof.

Likewise, to find the top consumers having accessed the most unique cultural content, it suffices to run this Cypher query: match ( dc : consumer ) -[ :ACCESS]->( c c : c o n t e n t ) return dc as account , count ( d i s t i n c t ( c c ) ) as amount l i m i t p Counting all the hits a consumer has ever done up to this point can be performed in the following way: match ( dc : consumer ) -[ r : ACCESS]->( c c : c o n t e n t ) where dc . name = ' name ' with d i s t i n c t ( r ) as r e l return sum ( r e l . h i t s )

The second group of graph cultural analytics dels with aspects of cultural vertrices. For instance, in a vertex about opera, one consumer may be particularly interested in the composer, one in the lyrics, and a third one in the year it was composed. Assuming that these aspects have been appropriately codified as a keyword in the kw property array of the respective vertex, then in addition the following relationship is created when the specific aspect is accessed for the first time: The above query raises the following three major architectural questions:

-When should a ASPECT::<aspect> be added or updated? We believe that the safest point to do is right after the corresponding ACCESS relationship has been added or updated. Even then an aspect is explicitly requested, again the ACCESS should be processed first. -Should there be a distinct ASPECT::<aspect> relationship or just the generic ACCESS enriched with independent counting for each aspect request? We believe that separating the ACCESS relationship will provide for faster analytics, since separate counting process can collect in parallel the ACCESS and the ASPECT::<relationship> relationships for all consumers. Moreover, seeking an aspect is semantically a different search from that of asking for the entire content. -Along a similar line of reasoning, should there be a separate and dedicated ASPECT::<aspect> for each aspect or an array property in a generic AS-PECT property along with a two-dimensional key-value array containing the aspects accessed so far and their respective hits? Extending our previous argument, we believe that Neo4j is primarily about handling relationships. Thus, respecting this fundamental design decision, our analytics will be more efficient, especially when computing values dependent on multiple aspects for all consumers. Additionally, the analytics can be written more naturally requiring less maintenance by taking advantage of the dynamic query formulation offered by py2neo.

The third and arguably more significant group of analytis deals with predicting the next cultural content request from a given digital consumer. Notice that the analytics themselves cannot be directly computed with Cypher queries, but they rely heavily on data collected by them. The one standing out is compiling a short personalized recommendation list for all digital consumers.

Here is presented an adaptive recommendation system combining past choices of the same consumer and similar content. The recommendations are updated after every decision, which in the long term yields statistically sound results. The list consists of q 0 ≡ 0 (mod 2) suggestions, which was six in our senarios -Out of them half were the most frequently accessed vertices in the t 0 decision past the current one, which in our case was three decisions ago. This small number not only saves memory space but it also consistent with the fact that a consumer will look for similar material over a short time window. These constitute sublist Q p . -The other half consists of the most similar vertices based on the vertices the consumer has seen so far excluding vertices already seen. These constitute sublist Q n , which represents the novelty factor of the system.

The top half is selected adaptively based on the decisions the consumer has made in the recent past: If more options were from past options, then Q p is the top of the list followed by Q n , otherwise it is vice versa.

The Cypher queries for the verices representing cultural content are the same mutatis mutandis. For instance, along a similar line of reasoning, a cultural content vertex is created using this Cypher query: merge ( c c : c o n t e n t , { i d : '<id> ' , kw : [ '<kw 1> ' , . . . ] } ) return c c Finally, to clear the entire dataset the following Cypher query can be issued: match ( v ) detach delete v Note that the above query is inefficient for large datasets, practically when the order of vertices exceeds a few thousands.

Implicit adjacency tensor format

Since each edge of the analytics graph, namely the graph stored at the database, is mandatorily labeled and there are multiple possible labels, it follows that said graph is by definition a multilayer graph as defined in [START_REF] Drakopoulos | Defining and evaluating Twitter influence metrics: A higher order approach in Neo4j[END_REF]. This type of graphs can be represented with adjacency tensors, which are direct generalizations of the adjacency matrices for simple graphs. Such a tensor T is defined as in equation [START_REF] Camerer | Behavioral economics: Past, present, future[END_REF] where n is the total number of vertices and e is the total number of edges:

T ∈ {0, 1} n×n×e (5)
Specifically, the element T [v i , v k , l j ] where 1 ≤ i, j ≤ n and 1 ≤ k ≤ e equals one if and only if there is an edge with label l k from v i to v j . Otherwise, it equals zero.

Notice that within the context of this work the adjacency tensor T is never explicitly constructed as it is never required, thus saving computational time. Nonetheless, the partitions of T are of great interest, since they reveal underlying connectivity patterns. A tensor layer can be obtained by fixing one of the three indexing integers. By focusing on the last integer, each resulting layer corresponds to an adjacency matrix formed by considering only a specific edge label. This yields two seprate cases.

Assuming there are n d consumer and n c content vertices with n = n d + n c and also that the consumer vertices are all placed in T before the content ones, then a layer with consumer-to-content analytics has the form:

T [:, :, k] = O n d ,n d A k O nc,n d O nc,nc , A k ∈ {0, 1} n d ×nc (6)
This layer, which is an adjacency matrix on its own right, corresponds to a special case of directed bipartite graph where the edge tails belong only to the consumer vertex set, denoted by V d , and the edge heads belong only to the content vertext set, denoted by V c . This is expected as only form of communication is that of a vertex v d ∈ V d reaching one or more vertices of V c . In this case, the maximum possible layer density happens when A k has non-zero entries and equals:

ρ 0 ≤ n d n c n 2 = n d (n -n d ) n 2 = n d n - n d n 2 = n d n 1 - n d n (7)
On the other hand, when the analytics are content-to-content, then the respective layer has the form:

T [:, :, k] = O n d ,n d O n d ,nc O nc,n d B k , B k ∈ {0, 1} nc×nc (8)
In these layers, matrices B k represent the dynamic arising from connectivity patterns only between vertex pairs of V c . In this case, the density ρ 0 of each layer is at most:

ρ 0 ≤ n 2 c n 2 = n c n 2 (9)
4 Results

Dataset

In order to evaluate in the proof of concept stage the proposed cultural analytics, a synthetic dataset has been created based on the preferences and features of real-world datasets about literature, opera, and theater. There are ten thousand requests in total from two thousand cultural consumers about nine hundred cultual vertices, with the three types of content being equally represented. Each request is a tuple of the form, where there may be up to three optional features:

(consumer, vertex[, aspect 1 , aspect 2 , aspect 3 ]) [START_REF] Drakopoulos | Tensor fusion of social structural and functional analytics over Neo4j[END_REF] The request distribution for each of three content types was Zipf with exponents in the rage of [START_REF] Bernheim | Handbook of Behavioral Economics: Foundations and Applications[END_REF][START_REF] Bloch | Language, anthropology, and cognitive science[END_REF] in order to ensure that the distributions were converging both in the mean value and in variance. The Zipf distribution models many humanderived activities such as the letter frequency of words in natural languages, voting [START_REF] Chatterjee | Universality in voting behavior: An empirical analysis[END_REF], and synthetic music generation [START_REF] Manaris | Evolutionary music and the Zipf-Mandelbrot law: Developing fitness functions for pleasant music[END_REF]. As a baseline, four feedforward neural networks (FFNNs) have been chosen whose respective configurations are shown in table 3. In this table each tuple entry corresponds to the total number of neurons of that given layer, whereas the number of entries is the number of layers. Always the first and the last layers are the input and output ones respectively, whereas any intermediate layers are hidden ones places in the order they are written. Layers are fully connected with each other, whereas the biases and the synaptic weights are randomly initialized. Each such network is fed a sequence of content vertex ids and the final output of an FFNN is alao such an id. In each hidden layer the activation function is the parametric SoftPlus function ϕ f (•), which is a smooth approximation to the ubiquitous ReLU function and additionally has the desired leaky property which avoids synaptic weight saturation. It is defined as:

ϕ f (u; α 0 , β 0 ) = ln (1 + exp (α 0 u + β 0 )) (11)
The activation function at the output layer neuron for the configurations FFNN1 and FFNN2 is the parametric sigmoid function ϕ s (•):

ϕ s (u; α 0 , β 0 ) = 1 1 + exp (-α 0 u + β 0 ) (12)
For the configurations FFNN3 and FFNN4 the activation function is the hyperbolic tangent function ϕ t (•):

ϕ t (u; α 0 , β 0 ) = α 0 tanh (β 0 u) (13)

Accuracy

Table 4 lists the accuracy of the proposed methodology with either the Tanimoto similarity coefficient (P1) or the cosine similarity coefficient (P2). From the results it can be seen that P2 achieves slightly better accuracy than P1. This can be attributed, partly at least, to the fact that P2 is based on a semantically aware method, even an indirect one. On the other hand, P1 is semantically oblivious, reducing the real semantic question to a set similarity problem and in turn to a mere matching problem. This conference paper presented a batch of fundamental graph analytics for cultural content recommendation expressed in the form of Neo4j Cypher queries. The latter are based not only on the past decisions of a specific digital consumer, as frequent selections within a certain window, but also on content similarity metrics. The latter rely on a set of keywords describing the aspects of other main properties of the specific piece of cultural content. On that set either a semantically oblivious or a semantically aware similarity metric can be applied in order to yield the final list of recommendations. Early results from a synthetic dataset indicate that the semantically aware similarity metric yields better results in terms of the prediction accuracy. Moreover, both methods outperform a baseline consisting of four configurations of a basic feedforward neural network. Thus, the proposed methodology can offer both better accuracy and interpretability, something neural networks typically lack. Currently the proposed methodology considers a number of consumer-tocontent or content-to-content analytics only. Therefore, the addition of consumerto-consumer analytics would be a significant addition, since various clusterings of the digital consumer set may yield improved recommedations based on the preferences of similar consumers. Moreover, time is going only forward and as a consequence no rollback methods are available to face possible system anomalies. Finaly, another direction worth exploring is incorporating semantics into possible consumer set similarity metrics, provided such a move has a meaning. European Union, European Regional Development Funds (ERDF), and by national funds of Greece and Italy.

  match ( dc : consumer ) -[ :ACCESS]->( c c : c o n t e n t ) where dc . name = ' name ' and c c . i d = ' i d ' merge ( dc ) -[ r :ASPECT : : '<a s p e c t > ' { h i t s : 1}]->( c c ) return r

Table 1 .

 1 Notation of this conference paper Tanimoto similarity coefficient for sets S1 and S2 c (d1, d2) Cosine similarity coefficient for vectors d1 and d2 On 1 ,n 2Zero matrix of dimensions n1 × n2

	Symbol	Meaning
	=	Definition or equality by definition
	{s1, . . . , sn} Set with elements s1, . . . , sn
	|S|	Set cardinality
	τ (S1, S2)

Table 2 .

 2 Keyword map to fundamental types. the newly created vertex. Also, the merge clause ensures that at most one unique vertex is create, as if for some reason a duplicate vertex is attempted to be inserted, then no new vertex will be added to the graph. On the other hand, in such a scenario the create clause would have created a duplicate vertex.

	Type	Keywords
	music	lyrics, opera, singer, instruments, soprano, music
	literature author, structure, ending, plot, twist, literature
	theater actor, director, lighting, performance, script, theater
	merge ( dc : consumer , {name : '<name> ' ,
	h i s t : [ '<c c i d 1 > ' , . . . , '<c c i d n > ' ]	,
	kw : [ '<kw 1> ' , . . . , '<kw n> ' ] } )
	return dc

case

Table 3 .

 3 Configurations of the FFNN.

	Name	FFNN1 FFNN2 FFNN3 FFNN4
	Structure 8:8:1	8:4:1	8:8:1	8:4:1

Table 4 .

 4 Accuracy for the next content selection.

	Method	P1	P2	FFNN1 FFNN2 FFNN3 FFNN4
	Accuracy (%) 87.33 89.15 79.94	81.48	82.41	84.72
	5 Conclusions And Future Work

Acknowledgment

This conference paper is funded by Interreg V-A Greece-Italy Programme 2014-2020 "Fostering capacities and networking of industrial liaison offices, exploitation of research results, and business support" (ILONET), co-funded by the


Bego Blanco
email: begona.blanco@ehu.eus

Rubén Solozabal
email: ruben.solozabal@ehu.eus

Aitor Sanchoyerto
email: aitor.sanchoyerto@ehu.eus

Javier López-Cuadrado

Elisa Jimeno
email: elisa.jimeno@atos.es

Miguel Catalan-Cid
email: miguel.catalan@i2cat.net

Intelligent orchestration of end-to-end network slices for the allocation of mission critical services over NFV architectures

Keywords: Network Slicing, Orchestration, NFV, Mission-Critical

Intelligent Orchestration of End-to-End Network Slices for the Allocation of Mission Critical Services over NFV Architectures 1 Introduction and related work

5G networks are expected to bring a new disrupting ecosystem, prompting the creation of innovative next generation vertical applications. To that end, one of the most awaited features is the provisioning and management of network slices tailored to the needs of each particular vertical industry and specific deployment.

In particular, Network Function Virtualization (NFV) is embraced as one of the key technologies that will allow the creation of customized network slices to meet different service requirements. The public safety sector will be one of the major beneficiaries of this technological development. Traditional mission critical applications expose tight QoS requirements, which find difficulties to be fulfilled by traditional network models. In consequence, traditional public safety networks have demanded private and dedicated network models, which eventually lead to an inefficient use of resources and spectrum. But now, network slicing through proper resource orchestration is making the network sharing model a reality.

The concept of network slicing was introduced by the Next Generation Mobile Network (NGMN) alliance within its whitepaper [START_REF]NGMN: Description of network slicing concept[END_REF]. Later, the 3GPP took the responsability of standardising this technology, defining the entities and the functionality required to manage network slicing [START_REF]3GPP: Study on management and orchestration of network slicing for next generation network[END_REF]. Presently, network slicing is integrated in the ETSI-NFV architecture [START_REF]ETSI: Network Function Virtualization (NFV). Architectural Framework[END_REF]. Current NFV standards [START_REF]ETSI: Report on Network Slicing Support with ETSI NFV Architecture Framework[END_REF] define the interaction between the network slice management functions defined by the 3GPP and the NFV Management and Orchestration (MANO) module, establishing the required connection between the network controllers and the NFV orchestration unit in order to perform the dynamic assignment of network resources.

However, the implementation of the concept of orchestrating a service slice within this standardized network architecture is still in a development phase. In this sense, there are some independent initiatives as [START_REF] Khalili | Network slicing-aware nfv orchestration for 5g service platforms[END_REF][7] [3][9] that are contributing to the creation of modules that complement the current MANO capabilities in order to orchestrate E2E slices.

In this paper, we present an NFV-based intelligent orchestration cycle with the capability of providing a set of shared resources to deal with the dynamic reconfiguration challenge. This orchestration cycle has been developed in the scope of H2020 5G ESSENCE project [START_REF]H2020 5G ESSENCE project[END_REF]. The slice concept introduced in 5G, along with the highly virtualised and software-based deployments, enables the automatic on-the-fly adjustment of the resource assignment to the changeable environment. This feature is of utmost importance in mission critical applications where sudden events can instantly alter the network requirements and priorities. For this reason, this work provides a comprehensive approach to demonstrating dynamic End-to-End (E2E) slices reconfiguration and service adaptation in a mission critical deployment.

The paper is organised as follows: Section 2 describes the orchestration cycle defined to dynamically adjust the end-to-end network slices in a NFV-based deployment. Next, Section 3 describes the validation scenario to later discuss the obtained results. Finally, Section 4 summarizes the main contributions and poses new research challenges that will be addressed in the future.

NFV-based intelligent orchestration cycle

NFV comes up driven by the telecommunications industry in order to enhance the deployment flexibility, foster the integration of new services within operators and also attain CAPEX/OPEX drawdowns.

However, the dynamic allocation of resources to separated and customised network slices still remains a challenge. This section describes a novel orchestration cycle providing new tools for automated E2E network slicing. The proposed orchestration cycle involves the monitoring system, the alert mitigation module and the execution of the mitigation actions.

Network architecture

The 5G ESSENCE approach, depicted in Fig. 1, takes the existing 5G architectures as a reference point, combining the 3GPP framework for network management in Radio Access Network (RAN) sharing scenarios and the ETSI NFV framework for managing virtualised network functions. Our architecture allows multiple network operators (tenants) to provide services to their users through a set of Cloud-enabled Small Cells (CESCs) deployed, owned and managed by a third party (i.e., the CESC provider). The CESC offers virtualised environment with computing, storage and radio resources at the edge of the mobile network. This cloud can also be 'sliced' to enable multi-tenancy.

Besides, the two-tier architecture of 5G ESSENCE is well aligned with the 5G architecture described by 5G-PPP, where the infrastructure programmability is identified as one key design paradigm for 5G. First, 5G ESSENCE achieves infrastructure programmability by leveraging the virtualised computation resources available in an Edge Datacenter (Edge DC). These resources are used for hosting VNFs tailored according to the needs of each tenant, on a per-slice basis. Second, the Main Datacenter (Main DC) allows centralising and softwarising the control plane of small cell functions to enable a more efficient utilisation of the radio resources coordinated among multiple CESCs.

We propose to enhance the orchestration functionalities adding more intelligence into the CESC Manager (CESCM) together with the NFV Orchestrator (NFVO). In particular, 5G ESSENCE provides a network monitoring and alert mitigation mechanism that supports and improves both the NFVO and RAN controlling functions. The event flow for the management of end-to-end slicing for a Mission-critical Push-to-talk (MCPTT) service is depicted in Fig. 2, and each component is further described in the following sections.

System and service monitoring and alert mitigation

The main objective of the Monitoring and Alert Mitigation system shown in Fig. 3 is to access the available information about the network elements and process it in order to conclude if and when a network reconfiguration is needed. The orchestration cycle begins with the collection of the monitored data through the exporters in each monitoring-enabled building block. The monitored data is stored in Prometheus, which is on charge of triggering the alerts as defined according to the different services and their Service Level Agreement (SLA). These alerts are defined to notify about an unexpected behaviour in the system and SLA violations.

It must be also noted that the monitoring of the Wi-Fi RAN controller relies on the federation of the Prometheus server installed in the component. Federation allows Prometheus to have a heritage of some targets monitored from another Prometheus. The main idea for using Federation is to have a decentralised system in order to monitor the Wi-Fi RAN metrics through another Prometheus for other tasks. The alerts raised are picked by the Alert Mitigation Module (AMM), which is part of the Management Monitoring Gateway. The purpose of AMM is to manage the configuration of the architectural components responsible of the behavior of the E2E slice. To that aim, AMM contains the mitigation logic based on a ponderation of the rules defined in the Rulebook. When an alert is triggered, AMM differences between different severity levels. If the severity is critical, the mitigation module must mitigate the alert with higher priority without considering further alerts following the configuration defined in the Rulebook. For warning severities, the Mitigation module saves the alert in a time window, which is configured by the Rulebook (Fig. 4). The window (or queue), groups the alerts by the specific mitigation required by it. Every warning alert has a ponderation in the Rulebook. The warning alert is added in the queue with its correspondent ponderation. If the sum of the ponderations in the mitigation queue exceeds the mitigation ponderation, configured in the Rulebook, a mitigation action composed with all the warning severity alerts is triggered, emptying the mitigation queue and silencing the alert.

Finally, within the scope of this paper, we have defined two endpoints to forward the mitigation actions and close the monitoring and mitigation loop: the NFVO orchestrator to manage the scaling options of the Network Service, and Wi-Fi RAN controller to manage the resources used by the Wi-Fi slice. These two blocks are further described in the next sections.

MCPTT service architecture

Mission Critical Push-To-Talk (MCPTT) is a mission critical communication standard that allows half duplex one-to-many and one-to-one voice services in order to coordinate emergency teams. Users request permission to transmit pressing a button. Additionally, the MCPTT service provides a means for a user with higher priority (e.g., MCPTT Emergency condition) to override (interrupt) the current speaker. As it appears, the management of this type of half-duplex communication is not trivial, since it requires an appropriate management of priorities and privileges to allow communication. The MCPTT Network Service is composed of one VNF that completes the mission critical push to talk service. This service is defined in multiple Virtual Deployment Units (VDU) to optimise the usage of the resources: a DNS server, an IMS (IP Multimedia Subsystem) service for session management, a CSC (Common Service Core) for service status information, and the MCPTT AS (Application Server) providing centralised support for MCPTT services and call control. Fig. 5 depicts the deployment of the described MCPTT network architecture.

In order to integrate the described MCPTT network service within the orchestration cycle detailed above, we must include a tailored exporter to extract the required metrics for the monitoring tasks. It appears as mcptt exporter in Fig. 5. This component is responsible for collecting the metrics from the MCPTT service to later expose them for the analysis in the monitoring system. It is implemented as a REST API: when the mcptt exporter receives a status request from the Prometheus in the Monitoring module, it queries the involved components of the NS (mainly IMS and MCPTT AS ) to gather the metrics and format them properly. Finally, the Wi-Fi CESC is composed by Single Board Computers (SBCs) with a Linux distribution. The main software used in order to deploy Wi-Fi connectivity is Hostapd, which has been modified in order to deploy, monitor and control multiple virtual Access Points (vAPs) on top of a single physical interface, according to the desired Wi-Fi slices. By means of these modifications, the Local Scheduler is able to manage the MAC-scheduler which controls the airtime or channel time assigned to each slice (which is then fairly distributed among all the user terminals of each slice). Also, it hosts a Prometheus Exporter (Hostapd Exporter1 ) in order to gather RAN telemetry.

RAN controller architecture

Orchestration cycle for MCPTT deployments

This section shows the results of the integration of the described enhanced orchestration tools developed within the 5G ESSENCE project to deploy a MCPTT service slice. To that aim, we first declare the metrics collected from the network elements and the mitigation actions defined when an emergency event is detected. Then, we describe the validation scenario and show the results of the complete deployment.

Monitoring metrics and mitigation action definition

The monitoring system collects network status information from network elements at different levels: NFVI through node exporter, MCPTT service through a tailored exporter and RACOON cSD-RAN controller through Prometheus federation. The information collected from the NFVI that is involved in this experiment includes CPU, memory and disk usages, VM port throughput and availability of VMs (if they are up). The information collected from the MCPTT service includes the number of registered and active users, the number of private calls, group calls, private emergency calls, group emergency calls that have been started/ongoing/terminated and the number of users involved in each of the calls. Finally, the system collects information from RACOON about the number of users per slice per cell, transmitted bit rate and quality of the signal. For each identified metric, the measurement framework and the alarms it can trigger is included. Two alarm thresholds are defined. The first alarm threshold provides a warning, whereas the second threshold is considered a critical situation.

Scenario definition and deployment results

The demonstration of the dynamically orchestrated MCPTT deployment cannot be based on a static scenario, since one of its objectives to be proven is the elastic allocation of resources attending to different levels of emergency conditions detected by the monitoring system. We propose a deployment topology in three main stages. Under normal circumstances, the system instantiates the network slices that correspond to a default service agreement. Here, the first responder only needs a reduced amount of access capacity and communication features for its normal operations. Then, triggered by an emergency incident that is detected through a private emergency call, the first responder requires increased capacity in terms of edge computing resources, in order to serve a higher number of incoming communications and/or public safety users. This implies the scaling of the MCPTT VNF and it may involve a deterioration of the service for legacy users, since their network slice(s) must be reduced in order to appropriately allocate the higher priority MCPTT service. Finally, in the third stage triggered by a group emergency call, the system responds with an expansion of the MCPTT radio slice up to the 75% of the available bandwidth in the cell where the emergency events are happening (detected by the increasing number of users in the cell). Again, this situation may involve an impairment of the service provided to civilians in favor of the communications for first responders, which require higher priority. Fig. 7 shows some screenshots that illustrate the operation of the orchestration process. The upper screen shows the monitoring of MCPTT calls during the experiment. It can be observed how the different events are detected over time. The screenshot in the middle shows the result of an alert mitigation action in the second stage that leads to the MCPTT VNF scale. Finally, the lower screenshot shows the reconfiguration of the radio slice as a result of the mitigation action in the third stage.

Conclusions and future work

This paper has described the intelligent orchestration cycle that proves that the 5G ESSENCE context provides a solution for an efficient and elastic E2E network slicing and the efficient orchestration of the radio and cloud resources. The results highlight the value of the shared network model, demonstrating the capacity of the 5G ESSENCE architecture to autonomously allocate resources to first responders whenever they are required, but giving them up to the commercial services when the requirements are low. The elastic allocation of resources is performed automatically, leveraging the monitoring and alert mitigation functionalities that complement the orchestration processes in the CESCM.

Our research work will continue to further develop orchestration tools to enhance the E2E slicing capabilities of NFV environments. New research trends include the use of machine learning techniques in the decision-making process, the migration and placement of VNFs and the analysis of the possibilities of multi-RAT access.

Fig. 1 .

 1 Fig. 1. 5G ESSENCE Network architecture.

Fig. 2 .

 2 Fig. 2. End-to-end slicing event flow.

Fig. 3 .

 3 Fig. 3. Monitoring and Alert mitigation architecture.

Fig. 4 .

 4 Fig. 4. Flow for mitigation warning alerts.

Fig. 5 .

 5 Fig. 5. MCPTT service architecture.

Fig. 6

 6 Fig. 6 depicts the components of the RACOON Wi-Fi RAN slicing solution.

Fig. 6 .

 6 Fig. 6. RACOON SD-RAN controller architecture.

Fig. 7 .

 7 Fig. 7. Orchestration cycle screenshots.

http://bitbucket.i2cat.net/users/miguel_catalan/repos/hostapd_ prometheus_exporter/browse

Acknowledgement

This work has been partly funded by the EU funded H2020 5G-PPP project 5G ESSENCE (Grant Agreement No 761592).