Quang-Minh Nguyen

Thanh-Tam Nguyen

Tuan-Dung Cao

Semantic-based Recommendation Method For Sport News Aggregation System

News on the Internet today plays an important role in helping people access daily information around the world. News aggregators are websites that collect and provide content from different sources in one location for easy viewing. However, the increasing number of news on the Internet makes it difficult for readers when they desire to access news they are concerned. One solution to this issue is based on employing recommender systems. In this research, we propose a novel method for news recommendation based on a combination of semantic similarity with content similarity between news and implement it as a feature of semantic-based news aggregators BKSport. Experimental results have shown that, a combination of both kind of similarity measures will result in better recommendation than when using either measure separately.

Introduction

The development of the Internet has brought a sharp increase in the number of news websites and the Web becomes a popular platform for broadcasting news. News aggregators are websites that collect news from various sources and provide an aggregated view of the events taking place in all over the world. Unfortunately, a critical issue of news aggregation systems is that large number of daily published news obstructs readers when they want to find the ones relevant to their particular interests. A possible solution to this problem is the use of recommender systems as they can traverse the space of choices and predict the potential usefulness of news for each reader.

There have been many researches on news recommendation methods which are based on a certain similarity measure, probably similarity between news with each other, known as Global Recommendation System (GRS), or similarity between personal interests of readers and news, known as Personal Recommendation System (PRS) [START_REF] Ahn | Open User Profiles for Adaptive News Systems: Help or Harm?[END_REF][START_REF] Billsus | A Personal News Agent that Talks, Learns and Explains[END_REF]. In GRS, news recommended are news with the highest similarity with news that readers are reading. On the other hand in PRS, news recommended for readers are news with the highest similarity with personal interests of readers, which is modeled based on the history of posts that readers have read. Collaborative filtering (CF) is a widely applied technology in PRS development. With explosion of news on the Web, designing novel approach for effective new recommendation to suggest news closer and more relevant to readers is still a matter of concern. In this research, we focus on proposing a news recommendation method according to just global recommendation system model by enhancing results from existing works.

The most important task in developing GRS systems is to build a model to calculate similarity between news. Recent research works on news similarity measuring center on two prominent approaches: content-based similarity and semantic-based similarity. In content-based approach, similarity of news is calculated based on vocabulary statistics appeared in content of news and almost all recommended news only focus on a subject that target news is about. In contrast, in semantic-based approach [START_REF] Abdelrahman | A Survey on Semantic Similarity Measures between Concepts in Health Domain[END_REF], similarity of news is usually based on a knowledge base available to exploit semantic relationship between elements appeared in these news. Therefore, recommended news will likely expand the subjects than that of content-based approach. Both approaches have some weaknesses limit, which limit their effectiveness in news recommendation. Our approach is a hybrid one in the sense that it combines content-based recommendation and semantic-based recommendation. In concrete, similarity of news is a linear combination of content-based similarity and semantic-based similarity. The experimental results indicate that this combination brings news results suggest more effective than using either measure separately.

This work is in a part of development research of News Aggregation System BKSport [START_REF] Tuan-Dung | Towards efficient sport data integration through semantic annotation[END_REF] that is based on Semantic Web technology, aiming to effectively handle the amount of sports news gathered from various sources on the internet. Therefore, it inherits results obtained in our previous research such as ontology and knowledge base in the sport domain, methods for named entity recognition and semantic relationships extraction between entities in the news.

The rest of the paper is organized as follows. Section 2 describes previous works related to measuring semantic similarity between news. Section 3 presents more in details of our proposed method. In Section 4, we present the experiments and the evaluation we performed using the implementation of the proposed recommender. Subsequently, advantages and disadvantages of this method, as well as corrective measures and future research lines are concluded in Section 5.

Related work

Traditionally, many content-based recommenders [START_REF] Elahi | Improvement TFIDF for News Document using efficient similarity[END_REF][START_REF] Huang | Similarity Measures for Text Document Clustering[END_REF] use term extraction methods like TF-IDF (Term Frequency-Inverse Document Frequency [START_REF] Salton | Term-weighting approaches in automatic text retrieval[END_REF]) in conjunction with the cosine similarity measure in order to compare the similarity between two documents. TF-IDF is used to measure the importance of a word in a document based on its frequency of occurrence in the entire document dataset (or corpus). After calculating TF-IDF value for each word in document, this metric is combined with Cosine measure or Jacard measure to calculate similarity between two documents. TF-IDF value of the word appeared in document is calculated by the following formula:

𝑇𝐹-𝐼𝐷𝐹 𝑖𝑗 = 𝑇𝐹 𝑖𝑗 × 𝐼𝐷𝐹 𝑖 In which: Then, document is represented as a vector 𝑉 𝑖 obtaining 𝑁 dimensional vector (With 𝑁 is the size of dictionary), value of each element of vector is TF-IDF value of the word. If the word in the dictionary does not belong to news, value of corresponding element in the vector is 0.

𝑇𝐹 𝑖𝑗 =
In semantic-based approach, previous studies have explored relationship between components between news with each other to calculate semantic similarity. In the study carried out by Batet et al. [START_REF] Batet | An Ontology-Based Measure to Compute Semantic Similarity in Biomedicine[END_REF], a measure based on the exploitation of the taxonomical structure of a biomedical ontology is proposed for determining the semantic similarity between word pairs. Method proposed by Michel Capelle et al. [START_REF] Capelle | Semantic News Recommendation Using WordNet and Bing Similarities[END_REF] exploited element of similarity between components (words or named entities) in news thereby calculating similarity between two news. To measure the similarity between two components, their proposed method relies on:

-WordNet Dictionary tree when components are words -denoted by 𝑠𝑖𝑚 𝑆𝑆 -PMI measure when components are named entitiesdenoted by 𝑠𝑖𝑚 𝐵𝑖𝑛𝑔 . This measure relates to the statistical frequency of occurrence of components and co-occurrence between them Final formula combines two 𝑠𝑖𝑚 𝑆𝑆 and 𝑠𝑖𝑚 𝐵𝑖𝑛𝑔 measures to calculate semantic similarity between two news as follows (α is correction parameter):

𝑠𝑖𝑚 𝐵𝑖𝑛𝑔𝑆𝑆 = 𝛼 × 𝑠𝑖𝑚 𝐵𝑖𝑛𝑔 + 1 -𝛼 × 𝑠𝑖𝑚 𝑆𝑆 Also exploiting the relationship between components in two news with each other, Frasincar et al. [START_REF] Frasincar | Ontology-based news recommendation[END_REF] presented a number of news recommendation methods in semanticbased approach. Similar to Capelle [START_REF] Capelle | Semantic News Recommendation Using WordNet and Bing Similarities[END_REF], their work aims to a personalized recommendation system. However user profile of the reader is also built based on the news that the reader has read and calculating similarity between user profile and a news is the same as calculating similarity between two news. Methods presented in this research used ontology and knowledge base to exploit semantic relationship between concepts, which are classes in the ontology. Experiment showed that Ranked Semantic Recommendation 2 is the most effective among them. However, it remains certain limitations that we will show in the following parts and propose method to overcome.

Similarity between news items

There are two main approaches in calculating similarity between text news items as content-based and semantic-based. Each approach has its own advantages and disadvantages. We aim to combine these two approaches by combining content-based similarity measure and semantic-based similarity measure with the expectation to overcome limitations of each approach, making recommendation more effective.

Semantic-based similarity

To calculate semantic similarity, we exploit mutual semantic relations between components in news item. These relations are determined based on ontology and knowledge base that we have built. We extract and analyze components in the news items including: entities, types of entities and semantic annotations. The next sections will present how to exploit these components in calculating semantic similarity between news items.

Semantic relation between entities

Specifically, in order to exploit relations between entities for calculating similarity between news items, we extend Ranked Semantic Recommendation 2 method as approved by Frasincar et al. [START_REF] Frasincar | Ontology-based news recommendation[END_REF]. In this method, the authors also used ontology and knowledge base to exploit the relations between entities. However, the method remains some limitations such as:

-It only considers direct relations between entities without considering indirect relations. -It does not consider the importance of entities as they appear in various positions in the news item (title, description, etc.) To overcome these above limitations, in Section 3.1.1.1, we present a method to calculate the relation weight between entities based on ontology and knowledge base. In addition, we combine the statistical method of co-occurrence of entities in the same news items in determining relation weight between entities, which is presented in Section 3.1.1.2. Finally, we present the method in which uses relation weights between entities in determining semantic similarity between news items in Section 3.1.1.3.

Relation weight between entities based on ontology and knowledge base

Aleman-Meza et al. presented the methods to calculate the ranking of Semantic Association based on Semantic Path between the two entities in order to determine the relation weight between entities [START_REF] Aleman-Meza | Context-Aware Semantic Association Ranking[END_REF]. Specifically, they define Semantic Association and Semantic Path as follows:

Definition: if two entities 𝑒 1 and 𝑒 𝑛 can be connected together by one or more sequences 𝑒 1 , 𝑃 1 , 𝑒 2 , 𝑃 2 , 𝑒 3 , 𝑃 3 , … , 𝑒 𝑛-1 , 𝑃 𝑛 -1 , 𝑒 𝑛 in an RDF graph; here, 𝑒 𝑖 , 1 ≤ 𝑖 ≤ 𝑛, is entities and 𝑃 𝑗 , 1 ≤ 𝑗 ≤ 𝑛 is relations in ontology, then we say there exists semantic relation between 𝑒 1 and 𝑒 𝑛 .

Sequence 𝑒 1 , 𝑃 1 , 𝑒 2 , 𝑃 2 , 𝑒 3 , 𝑃 3 , … , 𝑒 𝑛-1 , 𝑃 𝑛-1 , 𝑒 𝑛 is a Semantic Path.

For example, in the knowledge base, we have: -<Lionel-Messi> <playFor> <Barcelona-FC> .

-< Luis-Suarez> <playFor> <Barcelona-FC>.

Then, there exists a semantic path between two entities Lionel Messi and Luis Suarez as follows: <Lionel-Messi>  <playFor>  <Barcelona-FC>  <playFor>  <Luis-Suarez> As a result, there exists a semantic relation between Lionel Messi and Luis Suarez.

Based on the properties of semantic path, we identify a path rank value to show the relation weight between two entities at both ends of the path. Because there might be multiple semantic paths between two entities, we get the highest path rank value to represent relation weight. Aleman-Meza et al. [START_REF] Aleman-Meza | Context-Aware Semantic Association Ranking[END_REF] used four characteristics of a semantic path to calculate path rank, corresponding to four following weights:

-Subsumption Weight: based on the structure of the ontology to determine component weight for each component (predicate and entity) in the path, thereby calculating weight for the whole path. -Path Length Weight: based on length of the path.

-Context Weight: based on determining which region each component of the path belongs to in the ontology. Each region in the ontology has a separate weight depending on the user's interests. -Trust Weight: based on weights of the properties in the ontology. Applying in news recommendation in football, we found that Path Length Weight and Trust Weight are two meaningful and appropriate weights. For this reason, we only use these two weights to determine path-rank of a semantic path.

Path Length Weight

Length of a semantic path 𝑒 1 , 𝑃 1 , 𝑒 2 , 𝑃 2 , 𝑒 3 , 𝑃 3 , … , 𝑒 𝑛-1 , 𝑃 𝑛-1 , 𝑒 𝑛 is the number of entities and relations in the path (exclude 𝑒 1 and 𝑒 𝑛 ). We can see that, when two entities remain indirect relation with each other through which the more there are entities and relations, the lower similarity between these two entities is. Consequently, path-rank of a semantic path must be inversely proportional to the length of that path. The Path Length Weight is defined in [START_REF] Aleman-Meza | Context-Aware Semantic Association Ranking[END_REF] as below:

𝑊 𝑙𝑒𝑛𝑔𝑡 = 1 𝑙𝑒𝑛𝑔𝑡 𝑝𝑎𝑡
In which: 𝑙𝑒𝑛𝑔𝑡 𝑝𝑎𝑡 is the length of semantic path. For example, we have two semantic paths:

-𝑃 1 : <Lionel-Messi>  <playFor>  <Barcelona-FC>  <competeIn>  <La-Liga>  <competeIn>  <Real-Madrid>  <playFor>  <Karim- Benzema> -𝑃 2 : <Lionel-Messi>  <playFor>  <Barcelona-FC>  <playFor>  <Luis-Suarez>
𝑃 1 has length of 7, we obtain:

𝑊 𝑙𝑒𝑛𝑔𝑡 (𝑃 1 ) = 1 𝑙𝑒𝑛𝑔𝑡 𝑝𝑎𝑡 = 1
7 𝑃 2 has length of 3, we obtain:

𝑊 𝑙𝑒𝑛𝑔𝑡 (𝑃 2 ) = 1 𝑙𝑒𝑛𝑔𝑡 𝑝𝑎𝑡 = 1 3
From there, we can see that similarity between Lionel Messi and Luis Suarez is higher than that between Lionel Messi and Karim Benzema.

Path Relation Weight

There are many different relations defined in the ontology. Every relation represents a different meaning therefore also represents a different relation weight between entities. Some relations show close association, some other relations express loose association. For example, we have two triplets in the knowledge base as below:

-<Luis-Enrique> <managerOf> <Barcelona-FC>.

-<Luis Suarez> <playFor> <Barcelona-FC>.

Here, there exist two relations which are relation <managerOf> and relation <playFor>. We can see that, relation <managerOf> shows more closer than relation <playFor>, because each team has only one single manager at a certain time; however, may have a lot of players. Therefore, we assign weight of <managerOf> higher than <playFor>. And for this reason, from above triplets, we conclude <Barcelona-FC> has higher similarity with <Luis-Enrique> than <Luis Suarez>. Weight of relations is in the range (0, 1]. Path Relation Weight of an overall path P is defined in [START_REF] Aleman-Meza | Context-Aware Semantic Association Ranking[END_REF] as below:

𝑊 𝑝𝑟𝑒𝑑𝑖𝑐𝑎𝑡𝑒 = 𝑤 𝑝 𝑝∈𝑝𝑎𝑡

Relation weight between two entities is based on ontology and knowledge base

Combining two weights 𝑊 𝑙𝑒𝑛𝑔 𝑡 and 𝑊 𝑝𝑟𝑒𝑑𝑖𝑐𝑎𝑡𝑒 by a pair of coefficients 𝛼 𝑤𝑙 and 𝛼 𝑤𝑝 , we define the path rank of a semantic path as below:

𝑊 𝑝𝑎𝑡 = 𝑊 𝑙𝑒𝑛𝑔𝑡 × 𝛼 𝑤𝑙 + 𝑊 𝑝𝑟𝑒𝑑𝑖𝑐𝑎𝑡𝑒 × 𝛼 𝑤𝑝 𝛼 𝑤𝑙 + 𝛼 𝑤𝑝 Value 𝑊 𝑝𝑎𝑡 in the above formula is also similarity value between two entities based on ontology and knowledge base.

Relation weight between entities based on statistics of co-occurrence in the same news items

According to the idea of the Michel Capelle et al. on PMI measure [START_REF] Capelle | Semantic News Recommendation Using WordNet and Bing Similarities[END_REF], if two entities co-occur in the same news items many times; these two entities have high similarity to each other. We count co-occurrence of named entity pairs in a dataset on football news to calculate weights PMI. The formula is defined as below:

𝑊 𝑃𝑀𝐼 𝑒 1 , 𝑒 2 = 𝑙𝑜𝑔 𝑐(𝑒 1 ,𝑒 2 ) 𝑁 𝑐(𝑒 1 ) 𝑁 × 𝑐(𝑒 2 )

𝑁

In which:

-𝑁 is the number of news items available in the dataset.

-𝑐(𝑒 1 , 𝑒 2 ) is the number of news items in the dataset that two entities 𝑢 and 𝑟 co-occur. -𝑐(𝑒 1 ) is the number of news items in the dataset containing entity 𝑒 1 , and 𝑐(𝑟) l is the number of news items in the dataset containing entity 𝑒 2 . As such, for each any entity pair, we have two values to calculate relation weights: Weight 𝑊 𝑝𝑎𝑡 (calculated based on semantic path) and weight 𝑊 𝑃𝑀𝐼 (calculated based on statistics of co-occurrence of entity pairs). Before combining these two weights with each other, we normalize them as below:

𝑤 𝑛𝑒𝑤 =
𝑤 𝑜𝑙𝑑 -𝑀𝐼𝑁 𝑀𝐴𝑋 -𝑀𝐼𝑁 In which: 𝑀𝐴𝑋 and 𝑀𝐼𝑁 corresponding are maximum value and minimum value in the value chain 𝑤.

Finally, we combine these two values together by a pair of coefficients 𝛽 𝑝𝑎𝑡 and 𝛽 𝑃𝑀𝐼 to calculate similarity of each entity pair as below:

𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑒𝑛𝑡𝑖𝑡𝑦 𝑒 1 , 𝑒 2 =
𝑊 𝑝𝑎𝑡 × 𝛽 𝑝𝑎𝑡 + 𝑊 𝑃𝑀𝐼 × 𝛽 𝑃𝑀𝐼 𝛽 𝑝𝑎𝑡 + 𝛽 𝑃𝑀𝐼 By convention, when 𝑒 1 ≡ 𝑒 2 then 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑒𝑛𝑡𝑖𝑡𝑦 𝑒 1 , 𝑒 2 = 1.

Method for calculating similarity between news items based on relation between entities

First of all, we define set of entities related to entity 𝑟 is a set containing entities that have similarity where 𝑟 is greater than 0 and denoted as below: Finally, we group two sets A and R to obtain set 𝐴 𝑅 called as expansion set of news item A:

𝑅(𝑟) = {𝑟 1 , 𝑟
𝐴 𝑅 = 𝐴 ∪ 𝑅
In the next step, we calculate ranking value for each entity in the set 𝐴 𝑅 . Each rating value will characterize the relevance of the entity corresponding to news item A. These ranking values should satisfy some properties:

-(1) If the more times an entity appears in the news item, the greater that entity's ranking value is. -(2) If the greater of entities in the news item that an entity is relevant to, the greater that entity's ranking value is. -(3) Ranking value also depends on appearance position of the entity in the news item. Regarding property (3), we determine an entity that can appear in the different positions of the news item, as follows: title, description, bolder-text (bold text, image title, etc.) and content. We also identify importance weight for these positions respectively as below: Finally, as the formula defined in [START_REF] Frasincar | Ontology-based news recommendation[END_REF], the ranking weight of each entity 𝑒 𝑗 in the set 𝐴 𝑅 is calculated by:

𝑊 𝑡𝑖𝑡𝑙𝑒 > 𝑊 𝑑𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛 > 𝑊 𝑏𝑜𝑙𝑑𝑒𝑟𝑡𝑒𝑥𝑡 > 𝑊 𝑐𝑜𝑛𝑡𝑒𝑛𝑡
𝑅𝑎𝑛𝑘 𝑒 𝑗 = 𝑖𝑗 𝑚 𝑖=1
Assume 𝑉 𝐴 is a vector containing above calculated 𝑅𝑎𝑛𝑘(𝑒 𝑖 ) values. We normalize values of each element in 𝑉 𝐴 in the range [0, 1]. Normalization formula is expressed as follows:

𝑣 𝑖 = 𝑣 𝑖 -𝑀𝐼𝑁 𝑀𝐴𝑋 -𝑀𝐼𝑁
In which MAX and MIN are maximum value and minimum value respectively of elements in vector 𝑉 𝐴 . If 𝑀𝐴𝑋 = 𝑀𝐼𝑁 ≠ 0 then 𝑣 𝑖 = 1, with every value of 𝑖.

As a result, taking all the steps above will obtain a vector for each news. Final step is calculating similarity between any two news based on their vectors.

Suppose we have two news A, B and two corresponding vectors 𝑉 𝐴 , 𝑉 𝐵 . Because these two vectors can have different number of dimensions, we define the similarity between two vectors 𝑉 𝐴 , 𝑉 𝐵 (also similarity between two news A and B) as a variation of cosine similarity as below:

𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑒𝑛𝑡𝑖𝑡𝑦 𝐴, 𝐵 = 𝑐𝑜𝑠𝑖𝑛𝑒 𝑉 𝐴 , 𝑉 𝐵 = 𝑣 𝑎 × 𝑣 𝑏 𝑒 𝑎 ∈𝐴, 𝑒 𝑏 ∈𝐵 𝑒 𝑎 ≡𝑒 𝑏 𝑣 𝑎 2 𝑒 𝑎 ∈𝐴 𝑎 2 × 𝑣 𝑏 2 𝑒 𝑏 ∈𝐵 𝑏 2
In which 𝑣 𝑎 , 𝑣 𝑏 corresponding are values 𝑅𝑎𝑛𝑘 𝑒 𝑎 , 𝑅𝑎𝑛𝑘(𝑒 𝑏 ) in vectors 𝑉 𝐴 , 𝑉 𝐵 .

Types of entities appeared in the news items

A reader who is interested in a subject is more likely to be also interested in other subjects of the same type. For example, if a reader is reading the news about football teams, then that reader tends to continue reading other news items about football teams rather than news items about players or stadiums. Therefore, if two news items have similarity in the types of entities, similarity of these two news items will be higher.

Fig. 1. An example of similarity between news based on types of entities in the news

In ontology, each named entity is defined in the knowledge base will belong to a certain object class defined. These classes can be regarded as the type of entity. For example, two entities Lionel Messi and Luis Suarez in the knowledge base have the same type, because they belong to class FootballPlayer; however, both are not the same type with entity Barcelona-FC because this entity belongs to FootballTeam.

Statistics of entity types appeared in the news items is similar to statistics of entities. Two different entities can be of the same type. Appearance position of entities also affects association weight between entity type and corresponding news item. These weights will be calculated based on appearance frequency and appearance position of entities of that type. Suppose, we calculate association weight for entity type 𝐶 for a news item 𝐴. Given that 𝑐 𝑖 is entities of class 𝐶 appeared in news item 𝐴, we define the association weight of entity type 𝐶 with news item 𝐴 as below:

𝑊𝐶 𝐶 = 𝑊𝐸(𝑐 𝑖 )
We build a vector for news item with elements as 𝑊𝐶 weights similar to building vector based on entity in section 3.1.1.3. Elements in each vector will be normalized before using variations of the formula for calculating similarity between vectors used in section 3.1.1.3. This value is denoted by 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑡𝑖𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑡𝑦𝑝𝑒 .

Semantic annotations of the news items

Semantic annotations here are triplets in the form of <subject> <predicate> <object>.

In which subject and object are two entities. These semantic annotations also play an important role because they represent somewhat content that news item is talking about.

Fig. 2. An example of similarity between news items based on semantic annotations of news

A news item may contain many triplets and a triplet may appear several times. Triplets appeared several times in the news item will be important triplets, showing main contents that news item mentions. Moreover, appearance position of these triplets in the news item also expresses their importance. The importance of positions in the news item (title, description, bolder-text, content) is similar to that presented in the previous section. The more common triplets of two news items, the higher their similarity is.

With each triplet, we denote 𝑁 𝑡𝑖𝑡𝑙𝑒 , 𝑁 𝑑𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛 , 𝑁 𝑏𝑜𝑙𝑑𝑒𝑟𝑡𝑒𝑥𝑡 , 𝑁 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 are numbers respectively of occurrences of this triplet in title, description, bolder-text, content. We use the same formula as the one for calculating importance weight of the entities in Section 3.1.1.3. to compute importance weight 𝑊𝑇 of each triplet in the news item. Then we represent these weights as elements of a vector then use vector normalization formula to put these weights in the range [0, 1]. To calculate similarity between news items based on semantic annotations, we use a variation of Cosine formula as described in Section 3. 1.1.3. to compute the distance between two vectors. This value is denoted by 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛 . Thus, we use three parameters to determine semantic similarity between news items, based on the following factors:

-Relations between named entities, -Types of entity in the news items, -Semantic annotations of the news items.

Each of these three parameters has different meanings in determining semantic similarity between news items. We combine these three parameters together to determine the final value showing semantic similarity between news items. To combine these three parameters, we use a set of three parameters including 𝜃 𝑒𝑛𝑡𝑖𝑡𝑦 , 𝜃 𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛 , 𝜃 𝑡𝑦𝑝𝑒 to express the level of importance of each of the above parameters. We define the final formula for calculating semantic similarity between two news items is as below:

𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑠𝑒𝑚𝑎 𝑛𝑡𝑖𝑐 𝐴, 𝐵 = 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑒𝑛𝑡𝑖𝑡𝑦 𝐴, 𝐵 × 𝜃 𝑒𝑛𝑡𝑖𝑡𝑦 + 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛 𝐴, 𝐵 × 𝜃 𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛 + 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑡𝑦𝑝𝑒 𝐴, 𝐵 × 𝜃 𝑡𝑦𝑝𝑒

Content-based similarity

With news recommendation method in which only uses semantic similarity as proposed above, we may encounter some problems as:

-Insufficient or incorrect identification of named entities that appear in the news item. -Insufficient semantic annotations of the news item. Occurrence of above limitations is caused by limited information in the ontology and knowledge base. This is unavoidable since the construction of ontology and knowledge base must be done manually or semi-automatically, so a lot of efforts need to be made. Furthermore, the evolution of real world knowledge, for example when new players come or players change their clubs, makes it difficult to timely update. To overpass these limitations, we combine the proposed semantic similarity and content similarity of two news items.

In this section we describe the content-based similarity which is computed using TF-IDF weight of words in the news item combined with cosine measure. Words with high TF-IDF weight are often important words, showing main contents of the news item. So, we are only interested in words with high TF-IDF weight. Steps to build a set of important words of the news item include:

-Step 1: Eliminate stop words. Stop words are words that do not make sense in the representation of contents of the news, such as: "a", "an", "the", etc. -Step 2: Standardize words into infinitive form. Verbs or nouns often exist in many different forms depending on the context, although they still express the same meanings. For example, "make", "makes" and "made". So, we will change them into infinitive form. -Step 3: Calculate TF-IDF for each word in the news (After being standardized in Step 2). -Step 4: Sort and select top words with the highest TF-IDF based on defined threshold. After above steps, we obtain a set of words with the highest TF-IDF. We represent news item in the form of a vector containing values 𝑣 𝑘 as TF-IDF value of words in the above set. Similarity measure between two news A and B with two important word sets 𝑆 𝐴 , 𝑆 𝐵 and two corresponding vectors 𝑉 𝐴 , 𝑉 𝐵 will be calculated based on variation of Cosine formula as below:

𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑇𝐹-𝐼𝐷𝐹 𝐴, 𝐵 = 𝑣 𝑎 × 𝑣 𝑏 𝑡 𝑎 ∈𝑆 𝐴 , 𝑡 𝑏 ∈𝑆 𝐵 𝑡 𝑎 ≡𝑡 𝑏 𝑣 𝑎 2 𝑡 𝑎 ∈𝑆 𝐴 𝑎 2 × 𝑣 𝑏 2 𝑡 𝑏 ∈𝑆 𝐵 𝑏 2
In which:

-𝑡 𝑎 , 𝑡 𝑏 are corresponding words in two sets 𝑆 𝐴 , 𝑆 𝐵 .

-𝑣 𝑎 , 𝑣 𝑏 are TF-IDF values of words 𝑡 𝑎 , 𝑡 𝑏 .

News recommendation algorithm with combined similarity

To combine semantic similarity 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐 with content similarity 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑇𝐹-𝐼𝐷𝐹 of two news items, we use pair of weights 𝛾 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐 and 𝛾 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 . We define the combination formula as below:

𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑 𝐴, 𝐵 = 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐 𝐴, 𝐵 × 𝛾 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐 + 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑇𝐹-𝐼𝐷𝐹 × 𝛾 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 News recommendation algorithm as below: Input: Target news item A and set N candidate news items C. Output: set of K news items with the highest semantic similarity with A.

- Assume that n t is the average number of tokens in a news item and n is the number of news items in dataset C. We see that, in step 1, the complexity of named entity recognition and semantic annotation of a news item is O(n c n t ), where n c is the total number of classes, entities and properties in ontology and knowledge base. Therefore, for n news items in the set C and a news item A, the time complexity of step 1 is O(nn c n t ). Step 2 transfers n+1 news items into vector TF-IDF. As we had computed the IDF for all tokens in the dictionary before running the algorithm, the time complexity of transferring a news item into a vector TF-IDF equal to the time complexity of calculate TF values for all tokens in that news item, O(n t ). Consequently the complexity of step 2 is O(nn t ). On the other hand, step 3 is repeated n times for each element in C. The steps from 3.1 to 3.4 are the multiplication of the pair of vectors TF-IDF, therefore, the time complexity of each iteration is O(n t ) and the time complexity of step 3 is O(nn t ). The time complexity of the sort algorithm in step 4 is O(nlogn). As a result, the time complexity of the proposed algorithm is O(nn c n t + nlogn).

Experiment and evaluation

Experiment scenario

The goal of this chapter is to evaluate and compare the effectiveness of three news recommendation methods:

-Only use semantic similarity between news items.

-Only use content similarity between news items.

-Combine both above similarities. The evaluation of the different methods is performed by measuring precision. Because we did not build an online system yet, so we use offline evaluation method for evaluation. For offline evaluation, we choose N=100 news items (symbolized as set 𝐴) from a number of famous sports websites such as http://www.skysports.com/ , http://www.espnfcasia.com/ , http://sports.yahoo.com/ and then we ask collaborators to rate that a news item as relevant or non-relevant with another one. After that, we have an experiment dataset in which each news item 𝐴 𝑖 will have 𝐾 𝐴 𝑖 (0 ≤ 𝐾 𝐴 𝑖 ≤ 𝑁 -1) related news items and (𝑁 -1 -𝐾 𝐴 𝑖 ) unrelated news items. We separately run methods above for each news item 𝐴 𝑖 in set 𝐴 and also generate 𝐾 𝐴 𝑖 news items with the highest similarity with it, then compared with 𝐾 𝐴 𝑖 news items that collaborators have identified in experiment dataset. For example, consider the news item 𝐴 1 , collaborators discover 5 news items in the remaining 99 news items related to 𝐴 1 , then algorithm automatically run also generated 5 corresponding news items, then compared them with 5 news items that collaborators have identified. Symbol:

-𝑇𝑃 𝐴 𝑖 is the number of news items that the algorithm precisely recommends for news item 𝐴 𝑖 . -𝐹𝑃 𝐴 𝑖 is the number of news items that the algorithm imprecisely recommends for news item 𝐴 𝑖 . -𝐹𝑁 𝐴 𝑖 is the number of related news items that the algorithm not recommend for news item 𝐴 𝑖 . We define precision for a news item 𝐴 𝑖 , using the following formula:

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝐴 𝑖 = 𝑇𝑃 𝐴 𝑖 𝑇𝑃 𝐴 𝑖 + 𝐹𝑃 𝐴 𝑖 = 𝑇𝑃 𝐴 𝑖 𝐾 𝐴 𝑖 Follow the way that we implement, we obtain 𝐹𝑃 𝐴 𝑖 = 𝐹𝑁 𝐴 𝑖 , then 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝐴 𝑖 = 𝑟𝑒𝑐𝑎𝑙𝑙(𝐴 𝑖 ). There for we only concern about 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 to evaluate these above methods. Finally, we define the final precision of the method as the average of precisions for the entire 𝑁 news items in the experiment dataset.

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛(𝐴) = 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛(𝐴 𝑖 )

𝐴 𝑖 ∈𝐴 𝑁

Experiment parameters

Certain parameters are employed to determine the importance of the components when these components are combined together. In this experiment, we set the value of parameters totally based on our point of view. For instance:

-Weights 𝑤 𝑝 of relations in the ontology to calculate 𝑊 𝑝𝑎𝑡 was assigned based on our perception on the relevance of each relation: 𝑤 𝑚𝑎𝑛𝑎𝑔𝑒𝑟𝑂𝑓 = 0.8, 𝑤 𝑝𝑙𝑎𝑦𝐹𝑜𝑟 = 0.6, 𝑤 𝑠𝑡𝑎𝑑𝑖𝑢𝑚𝑂𝑓 = 0.5, … -𝛾 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐 and 𝛾 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 are two parameters used when combining semantic similarity measure and content similarity measure between news items. As we consider the importance of content similarity is higher than the one of semantic similarity in news recommendation, we choose 𝛾 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐 = 1, 𝛾 𝑐𝑜𝑛𝑡 𝑒𝑛𝑡 = 2.

Experiment results and evaluation

After running three separate methods for set 𝐴 containing 100 news items as experiment scenario as presented in section 4.1, we obtain precision result of each method shown in Table 1.

Assessment of experiment results

Table 1 indicated that, for the experiment data 𝐴 containing 100 news items, the semantic-based recommendation method is not as precise as the content-based recommendation method. Meanwhile, if combining the content-based similarity method and semantic-based similarity method, it will bring the best results. This can be explained as follows: -When using only the semantic-based similarity (semantic-based approach), it is mainly dependent on the entities in the news items. Therefore, in some case, the algorithm recommends correct news items about the relevant entities but the completely different topic. For some collaborators, they will seem as irrelevant. -Following the content-based approach, the recommended news item's topic is usually quite close to the target news item. However, this method does not have the ability to expand the topic. If we have two news items about Barcelona club in which the first news item is about the play of the Club and the second one is about the transfer of the Club's players, the content-based approach will determines that the similarity of these news items is low. -When combining the content-based similarity and semantic-based similarity, the recommended news will overcome the limitations of each separated measure, leading to more efficient recommendation.

Conclusions and future work

In this research, we presented a recommendation method based on the combination of the content-based similarity and semantic-based similarity of the news items. The semantic-based measure is calculated based on the semantic relation among objects. It enables the recommendation not only stopping at the suggestion of the similar topic news items or news items rounding a key object of the target news item, but also being able to recommend the news items of other objects that these objects have a semantic relation with other ones in the target news item. However, the similarity measure is mainly focused on the entities and not considered the context mentioned in the news item. The content-based measure will overcome the weakness of semantic-based measure by extracting from the news item the words having the highest TF-IDF value and these words are characterized the main context mentioned in the news item. We evaluated and compared the precision of the proposed method and the recommendation method when using only either measure separately. The experimental results showed that the combination of the two similarities helps to promote the effectiveness of both and overcome the weaknesses of each other method, ultimately increasing the better recommendation. However the proposed method remains some limitations such as its dependency on the adequacy of the knowledge base and ontology. Determining the weights in such a way so that the combination of the measures achieves the highest efficiency is also a difficult problem to be solved of the method.

  2 , 𝑟 3 , … , 𝑟 𝑛 } Suppose there is a news item A, set of recognizable named entities in news item A is denoted as below: 𝐴 = {𝑎 1 , 𝑎 2 , 𝑎 3 , … , 𝑎 𝑚 } With each entity 𝑎 𝑖 in set A, we build a set of entities related to 𝑎 𝑖 corresponding to 𝑅 𝑎 𝑖 = {𝑎 𝑖1 , 𝑎 𝑖2 , 𝑎 𝑖3 , … , 𝑎 𝑖𝑘 }. Grouping all sets 𝑅(𝑎 𝑖 ) together (𝑖: 1 → 𝑚), we obtain set of all entities not included in A, but related to A:

	𝑅 =	𝑅(𝑎 𝑖 )

𝑖:1→𝑚

  To calculate the ranking value for each entity in the set 𝐴 𝑅 , based on Ranked Semantic Recommendation 2 technique[START_REF] Frasincar | Ontology-based news recommendation[END_REF], we also represent entities in a matrix, in which the first row represents entities in the set 𝐴 𝑅 and the first column represents entities in the set A. Matrix takes the following form:In above matrix, we calculate the value 𝑖𝑗 as below:𝑖𝑗 = 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑎 𝑖 , 𝑒 𝑗 ) × 𝑊𝐸(𝑎 𝑖 )In which 𝑊𝐸(𝑎 𝑖 ) is importance weight of the entity 𝑎 𝑖 in the news. This weight is calculated as follows: Suppose 𝑎 𝑖 is an entity appeared in the news item, and 𝑁 𝑡𝑖𝑡𝑙𝑒 , 𝑁 𝑑𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛 , 𝑁 𝑏𝑜𝑙𝑑𝑒𝑟𝑡𝑒𝑥𝑡 , 𝑁 𝑐𝑜𝑛 𝑡𝑒𝑛𝑡 are respectively numbers of occurrences of 𝑎 𝑖 in the title, description, bolder-text and content of the news item. We define the importance weight of entity 𝑎 𝑖 as below: 𝑊𝐸 𝑎 𝑖 = 𝑁 𝑡𝑖𝑡𝑙𝑒 × 𝑊 𝑡𝑖𝑡𝑙𝑒 + 𝑁 𝑑𝑒𝑠𝑐𝑟𝑖𝑝𝑡 𝑖𝑜𝑛 × 𝑊 𝑑𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛 +𝑁 𝑏𝑜𝑙𝑑𝑒𝑟𝑡𝑒𝑥𝑡 × 𝑊 𝑏𝑜𝑙𝑑𝑒𝑟𝑡𝑒𝑥𝑡 + 𝑁 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 × 𝑊 𝑐𝑜𝑛𝑡𝑒𝑛𝑡

	𝒆 𝟏	𝒆 𝟐 … 𝒆 𝒒
	𝒂 𝟏 11 12 … 1𝑞
	𝒂 𝟐 21 22 … 2𝑞
	… …	…	… 3𝑞
	𝒂 𝒎 𝑚1 𝑚2 … 4𝑞

  Step 1: Identify named entities, make semantic annotations for news item A and candidate news items in set C. -Step 2: Build set of words with the highest TF-IDF weight for news item A and news items in set C. -Step 3: With each news 𝐶 𝑖 in set C, take the following steps: o Step 3.1: Calculate 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑒𝑛𝑡𝑖𝑡𝑦 (𝐴, 𝐶 𝑖 ) o Step 3.2: Calculate 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛 (𝐴, 𝐶 𝑖 ) o Step 3.3: Calculate 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑏𝑎𝑠𝑒𝑑 -𝑡𝑦𝑝𝑒 (𝐴, 𝐶 𝑖 ) o Step 3.4: Calculate 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑠𝑒𝑚𝑎𝑛𝑡𝑖𝑐 𝐴, 𝐶 𝑖 based on the results of steps 3.1, 3.2 and 3.3. o Step 3.4: Calculate 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑇𝐹-𝐼𝐷𝐹 (𝐴, 𝐶 𝑖 ) o Step 3.5: Calculate 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑 𝐴, 𝐶 𝑖 based on the results of steps 3.4 and 3.5. -Step 4: Sort news items 𝐶 𝑖 in descending order according to value 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑 (𝐴, 𝐶 𝑖 ). Step 5: Get k news items in the top of the list sorted in Step 4 to recommend for news item A.

Table 1 .

 1 News recommendation precision in circumstances

	Precision


Nedim Dedić

Clare Stanier

Measuring the success of changes to existing Business Intelligence solutions to improve Business Intelligence reporting

Keywords: Business Intelligence, Measuring Success, User Satisfaction, Technical Functionality, Reports

To objectively evaluate the success of alterations to existing Business Intelligence (BI) environments, we need a way to compare measures from altered and unaltered versions of applications. The focus of this paper is on producing an evaluation tool which can be used to measure the success of amendments or updates made to existing BI solutions to support improved BI reporting. We define what we understand by success in this context, we elicit appropriate clusters of measurements together with the factors to be used for measuring success, and we develop an evaluation tool to be used by relevant stakeholders to measure success. We validate the evaluation tool with relevant domain experts and key users and make suggestions for future work.

Introduction

Improved decision-making, increased profit and market efficiency, and reduced costs are some of the potential benefits of improving existing analytical applications, such as Business Intelligence (BI), within an organisation. However, to measure the success of changes to existing applications, it is necessary to evaluate the changes and compare satisfaction measures for the original and the amended versions of that application. The focus of this paper is on measuring the success of changes made to BI systems from reporting perspective. The aims of this paper are: (i) to define what we understand by success in this context (ii) to contribute to knowledge by defining criteria to be used for measuring the success of BI improvements to enable more optimal reporting and (iii) to develop an evaluation tool to be used by relevant stakeholders to measure success. The paper is structured as follows: in section 2 we discuss BI and BI reporting. Section 3 reviews measurement in BI, looking at end user satisfaction and technical functionality. Section 4 discusses the development of the evaluation tool and section 5 presents conclusions and recommendations for future work.

2

Measuring changes to BI Reporting Processes

Business Intelligence

BI is seen as providing competitive advantage [START_REF] Olszak | Business Intelligence Systems in the holistic infrastructure development supporting decision-making in organisations[END_REF][START_REF] Marchand | Researching performance measurement systems: An information systems perspective[END_REF][START_REF] Brannon | Business Intelligence and E-Discovery[END_REF][START_REF] Alexander | Case Studies: Business intelligence[END_REF][START_REF] Thamir | Business Intelligence Capabilities and Implementation Strategies[END_REF] and essential for strategic decisionmaking [START_REF] Popovič | Conceptual Model of Business Value of Business Intelligence Systems[END_REF] and business analysis [START_REF] Kurniawan | Application of Business Intelligence to support marketing strategies: A Case Study Approach[END_REF]. There are a range of definitions of BI, some focus primarily on the goals of BI [START_REF] Luhn | A Business Intelligence System[END_REF][START_REF] Power | Decision Support Systems: Concepts and Resources for Managers[END_REF][START_REF] Lönnqvist | The Measurement of Business Intelligence[END_REF], others additionally discussing the structures and processes of BI [START_REF] Brannon | Business Intelligence and E-Discovery[END_REF][START_REF] Moss | Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-support Applications[END_REF][START_REF] Golfarelli | Beyond data warehousing: what's next in business intelligence?[END_REF][START_REF] Dekkers | Organising for Business Intelligence: A framework for aligning the use and development of information[END_REF][START_REF] Kimball | The Data Warehouse Lifecycle Toolkit 2nd ed[END_REF][START_REF] Jamaludin | Review on Business Intelligence "BI" Success Determinants in Project Implementation[END_REF], and others seeing BI more as an umbrella term which should be understood to include all the elements that make up the BI environment [START_REF] Turban | Business Intelligence: A Managerial Approach 2nd ed[END_REF]. In this paper, we understand BI as a term which includes the strategies, processes, applications, data, products, technologies and technical architectures used to support the collection, analysis, presentation and dissemination of business information. The focus in this paper is on the reporting layer. In the BI environment, data presentation and visualisation happens at the reporting layer through the use of BI reports, dashboard or queries. The reporting layer is one of the core concepts underlying BI [START_REF] Kimball | The Data Warehouse Lifecycle Toolkit 2nd ed[END_REF][START_REF] Inmon | Building the Data Warehouse 4th ed[END_REF][START_REF] Watson | The Current State of Business Intelligence[END_REF][START_REF] Baars | Management Support with Structured and Unstructured Data-An Integrated Business Intelligence Framework[END_REF][START_REF] Ranjan | Business Intelligence: Concepts, Components, Techniques and Benefits[END_REF][21][START_REF] Chu | A Framework for BI Systems Implementation in Manufacturing[END_REF][START_REF] Anadiotis | Agile business intelligence: reshaping the landscape[END_REF][START_REF] Obeidat | Business Intelligence Technology, Applications, and Trends[END_REF][START_REF] Imhoff | Mastering Data Warehouse Design: Relational and Dimensional Techniques[END_REF]. It provides users with meaningful operational data [START_REF] Mykitychyn | Assessing the Maturity of Information Architectures for Complex Dynamic Enterprise Systems[END_REF], which may be predefined queries in the form of standard reports or user defined reports based on self-service BI [START_REF] Rajesh | Supply Chain Management for Retailing[END_REF]. There is constant management pressure to justify the contribution of BI [START_REF] Sedera | User Satisfaction: An Overarching Measure of Enterprise System Success[END_REF] and this leads in turn to a demand for data about the role and uses of BI. As enterprises need fast and accurate assessment of market needs, and quick decision making offers competitive advantage, reporting and analytical support becomes critical for enterprises [START_REF] Olszak | Critical success factors for implementing Business Intelligence systems in small and medium enterprises on the example of Upper Silesia, Poland[END_REF].

Measuring success in BI

Many organisations struggle to define and measure BI success as there are numerous critical factors to be considered, such as BI capability, data quality, integration with other systems, flexibility, user access and risk management support [START_REF] Işik | Business intelligence success: The roles of BI capabilities and decision environments[END_REF]. In this paper, we adopt an existing approach that proposes measuring success in BI as "[the] positive benefits organisations could achieve by applying proposed modification in their BI environment" [START_REF] Işik | Business intelligence success: The roles of BI capabilities and decision environments[END_REF], and adapt it to consider BI reporting changes to be successful only if the changes provide or improve a positive experience for users.

DeLone and McLean proposed the well-known D&M IS Success Model to measure Information Systems (IS) success [START_REF] Delone | Information Systems Success: The Quest for the Dependent Variable[END_REF]. The D&M model was based on a comprehensive literature survey but was not empirically tested [START_REF] Sabherwal | SCIENCE Information Success : Individual Determinants Organisational System[END_REF]. In their initial model, which was later slightly amended [START_REF] Delone | The DeLone and McLean Model of Information Systems Success: A Ten-Year Update[END_REF][START_REF] Petter | Information Systems Success: The Quest for the Independent Variables[END_REF], DeLone and McLean wanted to synthesize previous research on IS success into coherent clusters. The D&M model, which is widely accepted, considers the dimensions of information quality, system quality, use, user satisfaction, organisational and individual aspect as relevant to IS success. The most current D&M model provides a list of IS success variable categories identifying some examples of key measures to be used in each category [START_REF] Petter | Information Systems Success: The Quest for the Independent Variables[END_REF]. For example: the variable category system quality could use measurements such as ease of use, system flexibility, system reliability, ease of learning, flexibility and response time; information quality could use measurements such as relevance, intelligibility, accuracy, usability and completeness; service quality measurements such as responsiveness, accuracy, reliability and technical competence; system use could use measurements such as amount, frequency, nature, extend and purpose of use; user satisfaction could be measured by single item or via multi-attribute scales; and net benefits could be measured through increased sales, cost reductions or improved productivity. The intention of the D&M model was to cover all possible IS success variables. In the context of this paper, the first question that arises is which factors from those dimensions (IS success variables) can be used as measures of success for BI projects. Can examples of key measures proposed by DeLone and McLean [START_REF] Delone | The DeLone and McLean Model of Information Systems Success: A Ten-Year Update[END_REF] as standard critical success factors (CSFs), be used to measure the success of system changes relevant for BI reporting? As BI is a branch of IS science, the logical answer seems to be, yes. However, to identify appropriate IS success variables from the D&M model and associated CSFs we have to focus on activities, phases and processes relevant for BI.

Measurements relevant to improve and manage existing BI processes

Measuring business performance has a long tradition in companies, and it can be useful in the case of BI to perform activities such as determining the actual value of BI to a company or to improve and manage existing BI processes [START_REF] Lönnqvist | The Measurement of Business Intelligence[END_REF]. Lönnqvist and Pirttimäki propose four phases to be considered when measuring the performance of BI:

(1) identification of information needs (2) information acquisition (3) information analysis and (4) storage and information utilisation [START_REF] Lönnqvist | The Measurement of Business Intelligence[END_REF]. The first phase considers activities related to discovering business information needed to resolve problems, the second acquisition of data from heterogeneous sources, and the third analysis of acquired data and wrapping them into information products [START_REF] Lönnqvist | The Measurement of Business Intelligence[END_REF]. The focus of this paper is on measuring the impact of BI system changes to BI reporting processes, meaning that the first three phases are outside the scope of the paper. Before decision makers can properly utilise information by applying reporting processes, it has to be adequately and timely communicated to the decision maker, making the fourth phase, namely storage and information utilisation, relevant for this paper.

Storage and information utilisation covers how to store, retrieve and share knowledge and information in the most optimal way, with business and other users, by using different BI applications, such as queries, reports and dashboards. Thus, it covers two clusters of measurements we identified as relevant: (i) business / end-users satisfaction, and (ii) technical functionality.

Business/ End Users Satisfaction

User satisfaction is recognised as a critical measure of the success of IS [START_REF] Delone | Information Systems Success: The Quest for the Dependent Variable[END_REF][START_REF] Delone | The DeLone and McLean Model of Information Systems Success: A Ten-Year Update[END_REF][START_REF] Petter | Information Systems Success: The Quest for the Independent Variables[END_REF][START_REF] Powers | MIS Project Management: Myths, Opinions, and Reality[END_REF][START_REF] Bailey | Development of a Tool for Measuring and Analyzing Computer User Satisfaction[END_REF][START_REF] Ives | The measurement of user information satisfaction[END_REF][START_REF] Doll | The Measurement of End-User Computing Satisfaction[END_REF][START_REF] Davison | Measuring the potential success of information system implementation[END_REF][START_REF] Chung-Kuang | Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: An empirical study of Taiwan's electronics industry[END_REF][START_REF] Dastgir | Factors Affecting the End-User Computing Satisfaction[END_REF][START_REF] Rahman | Measuring Performance for Data Warehouses-A Balanced Scorecard Approach[END_REF].

User satisfaction has been seen as a surrogate measure of IS effectiveness [START_REF] Gatian | Is user satisfaction a valid measure of system effectiveness[END_REF] and is one of most extensively used aspects for the evaluation of IS success [START_REF] Sedera | User Satisfaction: An Overarching Measure of Enterprise System Success[END_REF]. Data Warehouse (DW) performance must be acceptable to the end user community [START_REF] Rahman | Measuring Performance for Data Warehouses-A Balanced Scorecard Approach[END_REF]. Consequently, performance of BI reporting solutions, such as reports and dashboards, needs to meet this criterion.

Doll and Torkzadeh defined user satisfaction as "an affective attitude towards a specific computer application by someone who interacts with the application directly" [START_REF] Doll | The Measurement of End-User Computing Satisfaction[END_REF]. For example, by positively influencing the end user experience, such as improving productivity or facilitating easier decision making, IS can cause a positive increment of user satisfaction. On the other side, by negatively influencing the end user experience, IS can lead to lower user satisfaction. User satisfaction can be seen as the sum of feelings or attitudes of a user toward a numbers of factors relevant for a specific situation [START_REF] Bailey | Development of a Tool for Measuring and Analyzing Computer User Satisfaction[END_REF].

We identified user satisfaction as one cluster of measurements that should be considered in relation to the success of BI reporting systems, however, it is important to define what is meant by user in this context. Davis and Olson distinguished between two user groups: users making decisions based on output of the system, and users entering information and preparing system reports [START_REF] Davis | Management Information Systems: Conceptual Foundations, Structure, and Development[END_REF]. According to Doll and Torkzadeh [START_REF] Doll | The Measurement of End-User Computing Satisfaction[END_REF] end-user satisfaction in computing can be evaluated in terms of both the primary and secondary user roles, thus, they merge these two groups defined by Davis and Olson into one.

We analysed relevant user roles in eight large companies, which utilise BI, and identified two different user roles that actually use reports to make their business decisions or to achieve their operational or everyday activities: Management and Business Users. [START_REF] Doll | The Measurement of End-User Computing Satisfaction[END_REF][START_REF] Chung-Kuang | Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: An empirical study of Taiwan's electronics industry[END_REF]. The model to measure end user computer satisfaction included twelve attributes in the form of questions covering five aspects: content, accuracy, format, ease of use and timeliness. This model is well validated and has been found to be generalizable across several IS applications; however, it has not been validated with users of BI [START_REF] Chung-Kuang | Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: An empirical study of Taiwan's electronics industry[END_REF].

Petter et al [START_REF] Petter | Information Systems Success: The Quest for the Independent Variables[END_REF] provide several examples of measuring user satisfaction aspects as a part of IS success based on the D&M IS Success Model [START_REF] Petter | Information Systems Success: The Quest for the Independent Variables[END_REF]. According to them, we can use single items to measure user satisfaction, semantic differential scales to assess attitudes and satisfaction with the system, or multi-attribute scales to measure user information satisfaction. However, we face three issues when considering this approach in the context of evaluating user satisfaction concerning changes to BI reporting systems. First is the fact that the discussion is about methods of measuring, rather than relevant measurements. The second issue is that this approach is designed for IS rather than the narrower spectrum of BI. The third issue is that this approach does not identify explicit measurements to be used to validate success when changes are made to BI reporting systems. Considering the D&M model in the context of this paper, we identify ease of use and flexibility as the measures of system quality possibly relevant when measuring user satisfaction.

In the Data Warehouse Balanced Scorecard Model (DWBSM), user perspective based on user satisfaction with data quality and query performance is defined as one of four aspects when measuring the success of the DW [START_REF] Rahman | Measuring Performance for Data Warehouses-A Balanced Scorecard Approach[END_REF]. DWBSM considers data quality, average query response time, data freshness and timeliness of information per service level agreement as key factors in determining user satisfaction. As DW are at the heart of BI systems [START_REF] Olszak | Business Intelligence Systems in the holistic infrastructure development supporting decision-making in organisations[END_REF][START_REF] Dedić | An Evaluation of the Challenges of Multilingualism in Data Warehouse Development[END_REF], those factors are relevant to evaluating the success of changes to BI reporting but are not comprehensive enough as they cover only one part of a BI system.

To develop a model for the measurement of success in changes to BI reporting systems, we combined elements from different approaches, cross tabulating the aspects and attributes of the EUCS model with the phases to be considered when measuring performance of BI discussed in section 3. Table 1 shows the initial results of the cross tabulation with areas of intersection marked with 'x', and where each number represents a phase to be considered when measuring performance of BI proposed by Lönnqvist and Pirttimäki. The questions shown in Table 1 were later modified following feedback, as discussed in section 4.

As discussed in section 3, only the storage and information utilisation phase (marked with number 4 in Table 1) from the Lönnqvist and Pirttimäki approach is relevant when measuring the success of changes to BI reporting systems to enable more optimal reporting. Based on the analysis given in Table 1, it is possible to extract a list of attributes (questions) to be used as user satisfaction measurements. We extracted eight key measures and modified these for use in the BI context. The elements identified from the EUCS model were extended to include three additional questions related to changing descriptive content (CDS) of BI reports. Descriptive content of the reports can include, but is not limited to, descriptions of categories, hierarchies or attributes, such as product, customer or location names descriptions. The most common cause of such requests for changes to descriptive content are errors in the descriptions and CDS issues are common with large and rapidly changing dimensions [START_REF] Dedić | An Evaluation of the Challenges of Multilingualism in Data Warehouse Development[END_REF].

Table 2 presents the questions developed from these measures, which were later revised following feedback during the initial phase of validation.

The design of the questions supports both an interview-based approach and a quantitative survey based approach. However, using only user satisfaction criteria is not sufficient to measure the success of modifications to reporting systems. Ease of use Is the system user friendly? x

Is the system easy to use? x

Timeliness

Do you get the information you need in time? x Does the system provide up-to-date information? x Table 2. User satisfaction questions to measure success of improving existing BI system 1 Does the information content of the reports meet your needs?

2 Are the BI system and reports accurate?

3 Are you satisfied with the accuracy of the BI system and the associated reports?

4 Do you think the output is presented in a useful format?

5 Are the BI system and associated reports user friendly?

6 Are the BI system and associated reports easy to use?

7 Do you get the information you need in time?

8 Do the BI system and associated reports provide up-to-date information?

9 Are you satisfied with the changing descriptive content (CDS) functionality?

10 Is the BI system flexible enough regarding CDS functionality?

11 Is CDS functionality fast enough to fulfil business requirements in a timely fashion?

Technical functionality

In section 2, we identified technical functionality as the second cluster of measurements that need to be considered when measuring the success of changes to BI reporting systems. To initiate and manage improvement activities for specific software solutions, it has been suggested that there should be sequential measurements of the quality attributes of product or process [START_REF] Florak | Practical Software Measurement: Measuring for Process Management and Improvement 1st ed[END_REF].

Measuring technical functionality.

In the DWBSM approach, the following technical key factors are identified: ETL code performance, batch cycles runtime, reporting & BI query runtime, agile development, testing and flawless deployment into production environment [START_REF] Rahman | Measuring Performance for Data Warehouses-A Balanced Scorecard Approach[END_REF]. We identify reporting & BI query runtime as relevant in the context of BI reporting. From the D&M IS success model, we extract the response time measure from the system quality cluster of IS success variables. Reporting and BI query runtime and response time both belong to the time category although they are differently named. However, to measure the technical success of modifications to BI reporting solutions, it is not enough to conclude that we only need to measure the time. We need a clear definition and extraction of each relevant BI technical element belonging to the time and other technical categories that should be evaluated. Table 3 shows the extracted time elements and includes elements related to memory use and technical scalability.

Table 3. Technical measurements of success to improve existing BI system 11 Is CDS functionality fast enough to fulfil business requirements in a timely fashion?

Producing an Evaluation tool to measure success of changing BI environment

As discussed in section 3, we elicited two clusters of measurements for use when evaluating the success of changes to BI reporting systems. The measurements identified in the user satisfaction and in technical functionality clusters are intended to be recorded at two stages: (i) in the existing BI environment -before implementing any changes, and (ii) after modification of existing BI system -in a new environment. By comparing their values, the result from both stages can then be used to evaluate the success of changes to the BI reporting system.

To produce a tool for use by relevant stakeholders, we merged both clusters of measurements into one and developed a questionnaire like evaluation tool. We conducted a pilot survey with 10 BI domain experts and report users. Based on the responses received, the questions shown in Table 2 were amended; questions 2 and 3 were merged, we amended questions 5 and 6 and we removed question 9 as surplus. We also added one additional question identified as highly important by business users relating to the exporting and sharing of content functionality. We added one additional technical question, relating to speed of execution time when drilling-down, conditioning, removing or adding columns in reports. The final list of factors is shown in Table 4.

We validated the proposed factors by carrying out a survey with 30 key users working in the BI field. All users were asked to complete the user satisfaction element of the survey. However, technical functionality factors are arguably comprehensible and relevant only for technical users; thus, answering this part of survey was optional and dependent on the respondent's expertise.

As we had series of questions and statements which needed to be validated, a Likert scale [START_REF] Likert | A technique for the measurement of attitudes[END_REF] was used, scoring each on a scale of 1 -5 (where 1 is less important and 5 is most important). In the original Likert scale approach, responses are combined to create an attitudinal measurement scale, thus performing data analysis on the composite score from those responses [START_REF] Boone | Analyzing Likert Data[END_REF]. However, our intention was to score each individual question or statement separately and to examine the views of users regarding each separate factor. We therefore used the concept of Likert-type items that supports using multiple questions as a part of the research instrument, but without combining the responses into composite values [START_REF] Boone | Analyzing Likert Data[END_REF][START_REF] Clason | Analyzing Data Measured by Individual Likert-Type Items[END_REF]. Likert-type items fall into the ordinal measurement scale; thus mode or median are recommended to measure central tendency [START_REF] Boone | Analyzing Likert Data[END_REF]. The results of our survey are presented in A free text question allowed survey respondents to suggest additional factors and this identified two additional questions that could be relevant to the measurement of user satisfaction:

-Description of the key figures is available, sufficient and easy accessible via BI reports?

-Functionality allowing further consolidation of existing information is available in BI reports?

It also elicited one additional factor that could be used to measuring technical satisfaction:

-How platform independent are BI reports (able to run on any PC, OS, Laptop or mobile advice)?

However, those three additional factors were not validated in the same way as the factors listed in Table 4, thus, we do not include them and propose Table 4 as the core evaluation tool. An advantage of the approach is that the tool can be customised and additional factors added by stakeholders, meaning that the additional features identified in the survey could be added by users if required.

The proposed tool is limited on reporting aspect in BI and on business user group. Possible extension would include consideration to the views of other user groups, such as conceptual or organizational. The tool focuses on changes to support BI reporting and is not suitable for use to measure success of changes in regard to data warehousing, data acquisition or data modelling aspects. The tool would be easier to use if provided as a web based tool.

The tool discussed in this paper provides a mechanism for measuring the success of changes made to reporting in BI systems. The use of the tool could be extended beyond evaluation of changes to BI reporting systems and could be used as a general benchmarking tool when evaluating different BI software from the reporting aspect. For example, business and especially key BI users could use proposed tool to benchmark and select the most suitable existing BI software for implementation in their organisation. The approach used here could also be extended for use with other elements such as the impact of changes in data warehousing, data acquisition or data modelling processes.

Conclusions and Future Work

The focus of this paper was on measuring the success of new approaches to changing and improving existing BI solutions to enable more optimal BI reporting. Consequently, we explained BI and defined what we understand by success in terms of changes to BI reporting, we elicited appropriate clusters, including criteria to be used for measuring such success and developed an evaluation tool to be used by relevant stakeholders to measure success. Finally, using a preliminary and a further survey we validated our finding with relevant domain expert and key users. Future work will consist of using the evaluation tool in a real world environment to measure success when amending BI systems to improve BI reporting. This will allow evaluation of the tool on a case study basis.

1

  Initial BI report or dashboard execution time 2 Query execution time 3 Re-execution time when changing report language, currency or unit 4 Time required to change erroneous descriptions of descriptive attributes / hierarchies 5 Database memory consumption 6 CPU memory usage during execution of: a) Initial BI report or dashboard; b) Query; c) Re-execution of report when changing language, currency or unit; 7 Technical scalability and support for integration of proposed solution in regard to existing environment 8 Flexibility and extensibility in regard to possible extension of the system in the future 10 Is the BI system flexible enough regarding CDS functionality?

Table 1 .

 1 Cross-tabulation of EUCS attributes and phases of measuring BI performance

			Phases of measuring BI
				performance [10]
	EUCS aspects and their attributes [38]
			1	2	3	4
	Content	Does the system provide the precise	x	x	x
		information you need?
		Does the information content meet your needs?	x	x	x	x
		Does the system provide reports that	x	x
		seem to be just about exactly what you need?
		Does the system provide sufficient information?	x	x
	Accuracy	Is the system accurate?				x
		Are you satisfied with the accuracy of the system?				x
	Format	Dou you think the output is presented in a useful format?				x
		Is the information clear?	x		x

Table 4 ,

 4 and are grouped into two clusters of measurements, namely user satisfaction and technical functionality, where each contains individual factors.As we see from Table4, no single question relevant to user satisfaction had mode or median less than 4, indicating that each question was considered important. No single technical factor had mode or median less than 3, showing a strong tendency towards considering each technical factor important. As expected, a larger percentage of users with a greater technical role commented on technical aspects than users with a greater business orientation. Users with a greater business orientation rated user satisfaction questions as more important than users with a greater technical role, and the same effect was found in relation to users with a greater technical role commenting on technical functionality factors.

Table 4 .

 4 Survey results based on Likert-type items


Josef Basl
email: basl@vse.cz

Enterprise Information Systems and Technologies in Czech Companies from the Perspective of Trends in Industry 4.0

Keywords: Enterprise information system, internet of things, industry 4.0, innovation, information and communication technology, cyber physical system

The paper deals with aspects of ICT innovation based on the development of the internet of things in industrial companies. The article presents the main results of a pilot survey carried out in a number of Czech companies. They show the current understanding of Industry 4.0 principles and the penetration these trends in companies, including the penetration level of the main IT trends and integration role of enterprise information systems application in Industry 4.0.

Introduction

Long-term forecasts and trends of global development show that information and communication technology will continue to play a leading role among innovation technologies. Trends such as big data and cloud computing are very important today, but it seems that they will remain very important over the next 10-15 years. For example, the document Global trends 2030 [START_REF]Global Trends 2030[END_REF] emphasizes ICT as one of four key technological areas:

• Information and communication technology • Technologies pertaining to the security of vital resources (food, water, and energy needs) • New health technologies • New manufacturing and automation technologies.

Manufacturing and automation technologies are crucial for the deployment of ICT and at the same time they also represent one of the key segments of the portfolio of the Czech economy with a strong influence on the Czech labour market [START_REF] Doucek | Human Capital in ICT -Competitivness ind Innovation Potential in ICT[END_REF]. The importance of ICT in the future is also emphasized in the survey done by the OECD [14] for example.

Theoretical background

There is no doubt that ICT play a key role in the development of the whole of society. ICT will be shaping the global economy, social, and military developments as well as the world community's actions pertaining to the environment by 2030. Information technology will provide global access and pervasive services; social media and cybersecurity will be large new markets. Among the TOP 10 strategic technology trends according to the Gartner Group are [START_REF]Gartner -Top 10 Strategic Technology Trends for 2016[END_REF]: Advanced system architecture and virtual reality are examples of symptoms of the new important wave of changes. The time when it was trendy to talk about Web 2.0 or Enterprise 2.0 is partly behind us. At that time Enterprise 2.0 was dedicated to the use of emergent social software platforms within companies, or between companies and their partners or customers. The tools and services that employ Web 2.0 techniques such as tagging, ratings, networking, user commenting and discussion, open creation and editing policies started to be used at that time. [START_REF]Research and Markets: Enterprise 2.0: Is It Time for Your Organization to Make the Transition[END_REF].

•
But the current ICT trends do not only emphasize the social networking role of the internet. The internet is now also a platform for communication among machines and products. The internet offers a complete solution that goes beyond the potential and possibilities of traditional manufacturing industries.

All these changes are very significant and this new wave of changes is called the new, fourth industrial revolution. This revolution has started changes and movements that have never been experienced in society before.

To better understand the term 'fourth industrial revolution', the main principles of the previous three industrial revolutions should be remembered. The first industrial revolution was based on steam power and mechanization in industry. The second revolution was caused by electricity and mass production and connected with 'hard automation'. The third industrial revolution was based on computers and it was connected with 'flexible automation'. Finally, the current fourth revolution is also based on ICT but is associated with 'cyber physical systems'.

Fields such as automation, robots or digitalization of everything are important, and again the internet plays a key role -in the form of the internet of things (IoT) or ra-ther the internet of everything [START_REF] Tao | IoT-Based intelligent perception and access of manufacturing resource toward cloud manufacturing[END_REF][START_REF] Jing | Security of the Internet of Things: perspectives and challenges[END_REF]. This is the reason why we sometimes call the changes of Industry 4.0, 'factory 4.0' [START_REF]The Industrial Internet Consortium: A Global Nonprofit Partnership of Industry, Government and Academia[END_REF] or 'smart factory' [START_REF] Wang | Implementing Smart Factory of Industrie 4.0: An Outlook[END_REF]. This designation is a continuation of the term 'digital factory' which has been used in previous years.

The basic principles of Industry 4.0 are therefore the connection of machines, work pieces and systems, and businesses are creating intelligent networks along the entire value chain that can control each other autonomously. Industry 4.0 is a way to improve production processes, to increase the productivity for batch size equal to 1, to reflect individual demands and short term wishes. It helps to reduce lead time and time to market. It helps to reduce product development time and ad-hoc networking within cyber-physical systems. It helps transparency in real time, to make faster and more flexible decision making, to archive global optimization in development and production.

Examples for Industry 4.0 could be machines which can predict failures and trigger maintenance processes autonomously, or self-organized logistics which react to unexpected changes in production. Cyber-Physical Systems (CPS) is integrations of computation and physical processes [START_REF] Lee | Cyber Physical Systems: Design Challenges[END_REF][START_REF] Yan | Enabling cyber-physical systems with machine-to-machine technologies[END_REF]. Industry 4.0 means making important efforts not only at a technological but also at a national level. A good example has been set by the German government. The German Federal Ministry for Education and Research currently offers 183 different documents related to this topic. For example, there is a project of the future, 'Assembly 4.0' which was presented with the project of the month award in 2016. Industry 4.0 was also proposed and adopted as a part of the 'High-Tech Strategy 2020 Action Plan' of the German government (Recommendations, 2013). The general expectation is a growth of Industry 4.0 in Germany until 2020 by about 1.7% each year -mainly in chemistry, manufacturing, ICT and farming.

Similar steps have been taken in other industrially developed countries like the USA (in the 'Industrial Internet' document [START_REF]The Industrial Internet Consortium: A Global Nonprofit Partnership of Industry, Government and Academia[END_REF] and China (in the 'Internet+' document [START_REF] Wang | Implementing Smart Factory of Industrie 4.0: An Outlook[END_REF] and in the ambitious plan 'Made in China 2025' [START_REF] Kenedy | Made in China 2025[END_REF]. The Chinese government declares here that the country is aiming at Industry 4.0 implementation.

It is very important to note that the Czech government also strongly supports the Industry 4.0 trends in the document 'The national strategy Industry 4.0' published in September 2015 [START_REF]National Iniative -Industry 4.0[END_REF]. It was prepared and guaranteed by the Czech Ministry for Industry and Trade. Not only technological trends are elaborated here, but the changes in the labor market are highlighted as well.

Methodology -Formulation of the Aim and Research Questions

This paper deals with a survey of the penetration of Industry 4.0 principles in Czech companies. The important questions concern the role of the selected IT trends and enterprise information system software applications within the Industry 4.0 framework now and in the near future (between 2 and 5 years). The other questions ask about the preparation of Czech companies for this new trend.

The motivation for this survey was not only the current technological trends but the published manufacturing studies oriented towards Industry 4.0 penetration -on the global level [START_REF]Industry 4.0 -The State of the Nations, INFOSYS[END_REF] and on the national level in Germany [1, [START_REF] Eisert | Sind Mittelständler auf Industrie 4.0 vorbereitet?[END_REF]15]. The first of these surveys was the most significant. It was done by Infosys -a leader in consulting, technology, outsourcing and next-generation services and by the Institute for Industrial Management at the University of Aachen in Germany. The survey analyzed more than 400 companies in industrially highly developed countries -China, France, Germany, the United Kingdom and the United States. It shows the level of maturity of Industry 4.0 and the key findings of this study are as follows [START_REF]Industry 4.0 -The State of the Nations, INFOSYS[END_REF]:

• 85% of manufacturing companies globally are aware of the potential of technologies for increasing asset efficiency • However, only 15% of enterprises surveyed have so far implemented dedicated strategies to this end by analyzing data from their machines • The research revealed that the largest improvements planned over the next five years are in the areas of information interoperability, data standardization and advanced analytics • It is interesting that one fifth of companies believe that by 2020 will achieve anything beyond recognizing the potential of the Industry 4.0 concept.

The results of the survey declare that from all the five analyzed countries (China, France, Germany, the UK and the US); China is the leading innovator and has the highest percentage of early adopters (57%). Germany is in fourth place with only 21% of early adopters. The German attitude and its wide support of Industry 4.0 is a big inspiration for the Czech economy and companies. There are many German investors and owners of companies in the Czech Republic and there is also close business cooperation between both countries, with a large volume of mutual exports.

Another reason for the survey described in this paper is an effort to obtain a more detailed view of the current ICT trends that are somehow connected with ICT such as mobile devices, clouds and big data on the one hand and ERP, MES, BI and APS applications on the other. Last but not least, topics such as robots, smart logistics and flexible production planning were also observed.

The further research question in this survey examines whether the strategies for Industry 4.0 are being implemented at the proper level in Czech companies, in a way comparable with the situation in Germany (as there is a high level of integration of companies from both countries).

The main research questions in this survey are as follows:

1) Are the main ICT trends (like cloud computing, big data and internet of things) being applied in the current development of companies and is growth of their penetration expected over the next 5 years? 2) Do ERP systems play a main role as an integrate application software? 3) Are the main trends of Industry 4.0 (such as robots or adaptive maintenance) being applied in the current development of companies and is 4) Do Czech companies already have a strategy for Industry 4.0?

Sample Description and Data Collection

To be able to answer the research questions, a special questionnaire form was created which was made available for the companies on the website. Data collection was carried out by completing the web form in June/July 2016.

A set of 169 companies was addressed by the survey. 24 companies answered, meaning that the survey had a 14.7% response rate. It is important that the sample of companies reflects well the profile of the whole Czech economy, because the majority of firms belong to the automotive industry (29%) and mechanical engineering (25%). The Czech Republic is, by the way, the most developed industrial EU country because its share of GDP from industry is 47.3% (Germany has only 40.2%).

The companies that participated in the survey were mostly large companies with more than 250 employees (66.7%) and middle sized companies (25%). There was also balanced ownership of domestic investors (58.3%) and foreign investors and owners (41.7%). The importance for the validity of the data was the fact that it was mostly the directors or company owners (33.3%) and top managers (41.7%) who answered the questions in the form. It is interesting that only 4.2% of IT managers answered the questions.

There is one more aspect that characterizes the survey. The majority of companies that answered and participated in the survey declared that they have dealt with Industry 4.0 for either more than one year (41.7%) or are dealing with it right now (20.8%). Only one fifth of the companies (20.8%) say that they know about this new trend but they do not want to implement it. On the other hand, only 8.3% of companies declared that they have not heard of Industry 4.0 so far.

Finally, there is one more interesting fact revealed by the survey. Those who answered the questions spent roughly one hour filling it in. This confirms the importance of the role of Industry 4.0. The main results of the survey and the answers are described below.

Research results

The results from the survey as a whole reflect the trends in the application of ICT (such as cloud computing and big data), IT supported technological trends (e.g. robots, predictive maintenance, digital manufacturing and smart logistics) and usability and integration of the traditional enterprise information systems packages (like ERP, MES and CRM for example) in the Industry 4.0 trends and principles.

ICT trends penetration

The first research question: Are the main IT trends applied (like cloud computing, big data, internet of things) in the current development of companies and is growth of its penetration expected over the next 5 years?

The results show that all the selected topics are already being applied today. Cloud computing mostly in first place, big data is second and the industrial internet of things is in third position.

It is interesting that demand and implementation of all these three trends is not predicted to grow in the next five years, although their penetration is the basis for this, and it is crucial.

IT trends

Used The industrial internet of things is one of the key aspects of the whole new wave of changes known as the 4.0 industrial revolution. Therefore, it is interesting that enterprises want to know more information about it -nearly one half of them (47.8%). The other two main trends (cloud computing and big data) are also topics which need to be further explained for nearly one third of the enterprises.

IT trends

We would like get more information Industrial Internet of Things ) and open protocols (56.5%) are the topics which companies would most like to have further explained.

Enterprise information systems packages integration in the Industry 4.0 perspective

The second research question: Do ERP systems play a main role as an integrate application software?

The results confirm the key role of ERP systems (65.2%) in the integration of plans of companies during the preparation for Industry 4.0. The next most important package is MES application.

Conclusion

Industry 4.0 seems to be a topic with a high potential, especially at a time when the digitalization of production and of life in general is increasing. The survey indicates many similarities between Industry 4.0 penetration in Czech companies and in leading developed countries.

The survey identified a big potential for further analyses and the obstacles preventing the wider application of Industry 4.0. The main reason for companies is that there is little awareness of the issues of Industry 4.0 (73.3% declare this), and the effects on business are unclear (40%). The high costs connected with implementing Industry 4.0 (40%) are also cited as one of the obstacles.

The survey shows that companies perceive the level of penetration to be very low and they also feel that there are as yet no proper methodologies or road maps for implementing Industry 4.0. These aspects are again possible further areas for research.

Last but not least is the fact that it has a crucial influence on the labour market. It is expected that many jobs will be lost because of Industry 4.0. This fact is more important for the Czech Republic, which experienced the biggest growth of jobs after the crisis, especially in manufacturing (OECD, 2015). It will therefore be necessary to keep a balance within Industry 4.0 developments from both technological and social perspectives.

  The digital mesh o The device mesh o Ambient user experience o 3D printing machines • Smart machines o Information of everything o Advanced machine learning o Autonomous agents and things • The new IT reality o Adaptive security architecture o Mesh app and service architecture o IoT architecture and platforms.

The third research question: Are the main trends of Industry 4.0 (robots, adaptive maintenance) being applied in the current development of companies and is growth of its penetration expected over the next 5 years?

The three most important IT based technologies are -robots (39.1%), digital factory (30.4%) and predictive maintenance (30.4%). The higher demand for the following 2 years is expected for predictive maintenance (34.8%) and smart logistics (26.1%). 3D printing is used extensively now and there are high expectations that it will be in use even in 5 years' time.

IT based technologies

Used The main topics for further education and requirements for additional awareness are ¨cybernetic data security (39.1%) followed by adaptive automation (34.8%), digital factory, predictive maintenance and industrial 3D printing (30.4%).

IT based technologies

We Cybernetic data security 9 (39.1 %)

Existence of an Industry 4.0 strategy

The fourth research question is: Do Czech companies have a strategy for Industry 4.0?

A high percentage of Czech enterprises (39.1%) do not currently have a strategy for Industry 4.0. Nearly the same percentage of enterprises is preparing such strategy (30.4%). And finally, nearly 25%, it means each fourth company, already has a strategy for Industry 4.0. This is very similar to the answers from firms in the global survey (by Infosys, 2015).