Amaresh Chakrabarti

Enhancing domain specific sentiment lexicon for issue identification

Keywords: Natural Language Text, Issues in assembly, Sentiment Analysis

The research work reported here is part of a larger project aimed at acquiring knowledge about issues in assembly, from documents. In order to do so, the first step is to identify the presence of issues. For this, sentiment analysis is proposed as a means. The presence of issues is proposed to be found by detecting negative sentiment. However, general English sentiment lexicons are not enough to detect negative sentiment in specialized domains, in this case, aircraft assembly. This paper studies an existing sentiment analysis tool, and creates an enhanced sentiment lexicon on the basis of the performance of the tool. The enhanced domain lexicon is proposed as the solution to identifying domain specific issues.

Introduction

Knowledge is an important asset in an organization and is generated through many complex processes. Hence it is useful to capture and reuse this knowledge, especially where it can improve time or costs. The aim of the research, which the present work is a part of, is to acquire knowledge about manufacturing -in particular, knowledge of assembly issues from documents. Such acquired knowledge is intended to be used to detect potential issues in current or future assembly plans. The domain under study is assembly of aircraft structures. The knowledge to be acquired is that of issues that arise in assembly stage of manufacturing. This knowledge is then expected to be fed back to assembly planners to foresee issues in their current assembly plans. In the context of a product's lifecycle, this amounts to reuse of knowledge from a later stage of the lifecycle in an earlier stage. Such a reuse of knowledge is an important factor for PLM systems [START_REF] Ameri | Product lifecycle management: closing the knowledge loops[END_REF].

The overall process of acquiring knowledge is shown in Figure 1, and the focus of this paper is shown in the dotted rectangle. From previous assembly processes, documents about problems in assembly may have been generated. These input documents are processed in the first step to segregate portions of documents that are related to aircraft assembly. Among these relevant portions, issues or problems that are present in the text are to be identified. This implies finding parts of text that talk about these issues in the domain. Once such issues are identified, the causes of these issues and parameters related to these causes are to be found. This knowledge about issues, their causes, and the parameters leading to the causes, should be structured as diagnostic knowledge. This structured knowledge would become the source for predicting assembly issues in the current assembly process.

Fig 1: Overview of the process to acquire knowledge of issues

To illustrate the knowledge reuse, consider an example where a document has been written about problems faced during an earlier assembly operation. An issue that a particular riveting gun does not provide enough force for clean riveting (although specifications say otherwise) is described in this document. Hence a riveting gun with a higher force specification is prescribed. If there is another assembly that is currently in the planning stage, and it also involves riveting, knowledge of this issue is relevant. Hence the planners can using a riveting gun of a higher force in practice and thus avoid a revision in assembly planning, due to this knowledge being available beforehand.

In order to identify issues from text, the first step is to identify sections that talk about issues. In an earlier paper [START_REF] Madhusudanan | Evaluation of methods to identify assembly issues in text[END_REF], the authors have evaluated various methods and finally identified two possible means for such an identification. As a more practical solution, the method of sentiment analysis, which is a set of natural language processing techniques, was chosen.

Sentiment Analysis

Sentiment analysis (SA) is a set of natural language processing techniques whose aim is to determine whether a given piece of text is carrying a positive, negative, or neutral sentiment [START_REF] Taboada | Lexicon-based methods for sentiment analysis[END_REF] (i.e. the sentiment polarity of the text), and a numeric value to indicate the strength of the sentiment. For example, 'happy' is positive, 'very happy' is more positive, and 'not happy' is negative. Sentiment analysis has found use in domains like movie reviews, and consumer electronics [START_REF] Blitzer | Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification[END_REF]. Sentiment can be calculated at various levels in text -at document, sentence, phrase or word-levels.

Research Problem

Although sentiment analysis is useful, adapting it to a specific domain is not a straightforward process. This process of adapting this technique to the domain of aircraft assembly is described in this paper. The contribution of this paper is a means of enhancing domain lexicon in the field of aircraft assembly, in order to detect negative sentiment in documents. Such a negative sentiment, it is hoped, will be linked to a potential problem description in the document of interest.

2

Tools for Sentiment Analysis

Different types of sentiment analysis tools

There are two major groups of sentiment analysis techniques, depending on the practical application [START_REF] Gonçalves | Comparing and combining sentiment analysis methods[END_REF]. The first group is that of supervised classification methods based on training with large amounts of positive and negative text. The second group is to construct lexicons of predefined words for a given sentiment task in a domain. Both these groups of methods have their distinct advantages and disadvantages. For example, the former is sensitive to training data supplied (and hence can adapt well to a domain, given enough data), but it also demands the availability of large amounts of data. The latter does not necessitate such labelling of data, but requires detailed study to build suitable sets of positive and negative words.

The need for large amount of training data is also prevalent in the domain of aircraft assembly. The currently available limited set of sources of documents are from the World Wide Web, and there is no single coherent source of such data.

Choice of tool

Due to the difficulties in finding sufficiently large training data in the domain of interest, we chose a lexicon-based approach to our task of identifying sections mentioning issues in text. From the available choices in this approach, two tools, namely SentiWordNet [START_REF] Baccianella | SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining[END_REF] and SO-CAL [START_REF] Taboada | Lexicon-based methods for sentiment analysis[END_REF] were considered. SentiWordNet is a lexicon based on WordNet with sentiment values assigned to words. SO-CAL, on the other hand, is based on a detailed theory of how sentiment is not just dependent on single words, but also gets modified, using valence shifters [START_REF] Polanyi | Contextual valence shifters[END_REF]. Since this could judge overall sentiment for sentences, SO-CAL has been used as the tool for Sentiment Analysis in this research. For further details on this tool, readers are referred to Taboada et al [START_REF] Taboada | Lexicon-based methods for sentiment analysis[END_REF].

As mentioned in the previous section, we now have a choice of a Sentiment Analysis (SA) tool to identify locations in text where issues are being described. The next step in the research was to verify if the lexicon, which was developed for a general English language texts (or for a different domain) would be still applicable to the domain of aircraft assembly. As mentioned in Kanayama and Nasukawa [START_REF] Kanayama | Fully automatic lexicon expansion for domain-oriented sentiment analysis[END_REF], it is more difficult to prepare domain dependent lexicons than domain independent ones. Fahrni and Klenner [START_REF] Fahrni | Old wine or warm beer: Target-specific sentiment analysis of adjectives[END_REF] have developed a combination of target nouns and adjectives that bear sentiment. This requires large, organized resources such as Wikipedia, and documents which bear the correct sentiment polarities (i.e. whether the text is positive, negative or neutral) for the objects. We are currently not having such resources for the domain of aircraft assembly. Denecke [START_REF] Denecke | Are SentiWordNet scores suited for multi-domain sentiment classification?[END_REF] compared lexicon based methods with machine learning based methods, concluding that the latter were better with SentiWordNet scores, doing well in multi-domain classification. However, sentence level sentiment was not studied, and machine learning based methods require labelled training data. Yue et al [START_REF] Lu | Automatic construction of a context-aware sentiment lexicon: an optimization approach[END_REF] proposed an optimization based method to learn target-specific sentiment words by combining multiple knowledge sources. It, however assumes the availability of aspects (set of words describing a topic), either from experts or from an automatic method, which requires it to be combined for operation. This method is also capable of handling clause level sentiment. Muhammed et al [START_REF] Muhammad | Domain-Based Lexicon Enhancement for Sentiment Analysis[END_REF] attempted to extend a general lexicon to a social media lexicon and then combining the general lexicon with the domain-specific lexicon. This, once again, was dependant on a distant supervision dataset to be labelled and available. Ohana et al [START_REF] Ohana | Domain independent sentiment classification with many lexicons[END_REF] suggest the use of many different lexicons with a score adjustment based on term frequencies, in order to improve domain-independent sentiment classification. However, there are no directions to generate a lexicon for a given domain, in the absence of one. As seen in the various methods discussed above there are several practical issues in adapting them, such as the availability of training data, or availability of other information that complements the lexicon building process. From a practical perspective, the simplest, yet useful method seemed to be to extend the lexicon manually for the chosen SA tool.

Study of existing lexicon on domain documents

In order to understand the current performance of the chosen SA tool on domain specific documents, a set of documents was initially chosen. These were documents available over the World Wide Web, and were about issues in manufacturing. SO-CAL was then used to analyze these documents, and the results were studied. For every sentence, the researchers compared the polarity of sentiment assigned, with what was perceived to be the actual polarity. It is important to state here that the strength of sentiment (how strongly positive/negative) could not be considered, as that requires considerably more subjects and efforts to arrive at commonly agreed numbers. A total of 357 sentences from 5 different documents were studied. Out of these, true positives and true negatives, as well as false positives and false negatives were identified. When we say "True Positive" here, it means that the sentence was positive in sentiment, and was also marked positive by the SA tool. The numbers are presented in Table 1. Since the original focus is to identify only negative sections of text, even sentences with SO-CAL score 0 were considered positive for this study (24 sentences were indecisive, hence they were not counted here). Some examples of these four categories were:

True Positive (TP): "A good rule to be used is that the number of blind rivets needs to be increased roughly in the proportion of 5 blind rivets for 3 solid rivets."

True Negative (TN): "In my opinion, this defeats the purpose of these rivets in the first place."

False Positive (FP): "But the mechanics say managers keep pressuring them to fix the planes faster."

False Negative (FN): "From race cars to airplanes, the blind rivet is the fastener of choice for joining sheet metal." (It may be noted that the word 'blind' triggers the classification as negative sentiment)

Inadequacies of general sentiment lexicons

As seen in Table 1, it was observed that there are large number cases where the negative (or positive) sentiment in a sentence is correctly identified by the SA tool. However, there were still other cases where it was not identified correctly (68 and 20 sentences for the positive and negative cases by tool). Each of these cases where the assigned sentiment did not match the opinion of the researchers was studied. The observations were classified into the following categories:

-Ambiguity of word meaning: The sense of a word differs, based on the context in which it is used. For example, the word 'issue' was marked negative even when it was used in the context of a magazine's date. Also, domain-specific use is a huge contributor to ambiguity, since a term that is used in general English would have a different meaning in manufacturing.

For example, a 'blind' rivet is not negative in meaning; 'upset' a rivet is not negative either; however, 'crossed' wires is negative. At times, this can be seen more as domain-specific meaning, rather than ambiguity. -Missing entries in the lexicon: There were many words in the SA tool's inbuilt lexicon which did not have a corresponding entry for sentiment score. Fortunately, SO-CAL provides a list of such missing entries which cannot be scored, for a corresponding Part-Of-Speech. Some examples were 'non-conformance', 'openness', and 'carcinogenic'. Also missing are certain entries that are phrases indicative of sentiment, such as 'got to our head' and 'build up'. -Clause level sentiment change: In the current work, the unit chosen is sentences, since the SA tool being used can handle sentences. However, even within sentences, there may be opposing sentiments in different clauses, which are finally summed up. For example, consider the following sentence: 'The program had been the gold standard of industrial design tools in the 1980s but was only capable of producing two-dimensional blueprints'.

In this sentence, the first part of the sentence appears to be largely positive whereas the second part is negative, and they are connected by what is called as the 'but' connective in literature [START_REF] Hatzivassiloglou | Predicting the semantic orientation of adjectives[END_REF].

Enhancing lexicon for domain specificity

The previous section described various reasons as to why a general sentiment lexicon did not perform as expected on text that is specific to a domain. In this section, we describe means of resolving some of these concerns. The list of missing entries was the first means for improving the lexicon to result in better sentiment analysis. SO-CAL outputs a list of words that could be resolved by its tagger, but marked as missing in its dictionaries. The list is classified into four categories on the basis of part-of-speech, namely nouns, verbs, adjectives and adverbs. For the initial set of 5 chosen documents, this list consisted of 2160 nouns, 1080 verbs, 484 adjectives, and 152 adverbs. This list was then collected and manually analyzed. The objective was to assign a single number (between -5 and +5) to each word that indicates its sentiment specific to the current domain. The sentiment values were only prior values, which meant they were values for context-independent use of the words.

Assignment of domain-specific sentiment values

Based on the above list, assignment of sentiment value for the list of words was to be chosen. Since there was no specific guideline that was available, we chose the following scheme to assign these values. Since the maximum sentiment value was +5 and minimum was -5, we decided to limit our values between +4 and -4 for the extreme cases, with one point for a buffer. Some generic guidelines were, -If the word indicates high efficiency, or solution to a problem, it was given a score of 4 ("much-lauded"). -If it reflects cause for improvements or progress, it was given a score of 2 ("completion"). -An object description of name was given a neutral score ("hydraulic").

-If something is not hazardous but still problematic, it got a score of -2 ("inaccessible"). -If there is a hazard involved the word got a score of -4 ("burst").

-Any word which was felt to be in between these categories was given an appropriate middle value, although such a value is subjective.

Evaluating the effects of adding user lexicon

The user specific dictionary was then tested. This first iteration of testing involved testing the effects only for the additional dictionary. The same set of documents were run through SO-CAL, after configuring it to use the additional dictionary. The results of this first iteration of testing are shown in Table 2 (Please note that the 'Zero'th iteration in the table refers to testing without any domain lexicon being added, from Table 1). There was improvement in terms of reduced False Positives and False Negatives.

There was also an increase in True negatives However, there was also a minor reduction in number of True Positives. The FN and FP cases were then analysed using the same approach described in Section 3.1. Multiple reasons were identified for the current performance of the tool. Some of the sentiment values needed a modification for the sentence level sentiment to be reflected correctly. In other cases, we realized that not only the specialized dictionary, but also the settings of the tool itself had played a role in deciding sentiment. These settings were related to the ignoring of sentiment words if they were in quotes or in irrealis mode (e.g. "may forget"). However, for our purposes both these modes were required to be present. Also, to a minor extent, some words from the existing SO-CAL dictionary itself had to be assigned a modified score, so that their prior sentiment is appropriate for the domain. In the second iteration, there were three changes made in the analysis: the modified extra dictionary added by the user (19 instances), ignoring the quotes and irrealis factors (15 instances), and a small number of modifications to the original sentiment dictionary (2 instances). SO-CAL was again re-run on the same test documents. The results can be found in the third row of Table 2.

It is observed that the best improvements are present in the True Negatives and False Positives. Between the initial state and the second iteration the number of true negatives has increased by 37 instances, which is a 10.3 % improvement over the total number of sentences. Similarly the number of false positives has fallen by the same number of instances.

Conclusions

This paper has discussed a method to improve the performance of a sentiment classification tool for the domain of aircraft assembly. Since there is a specific purpose for which sentiment analysis was used (to detect the presence of issues), the study focused more on the negative sentiment identification.

The study led to two means of improving the performance of the tool for the domain of aircraft assembly. The first is the construction of a dictionary of sentiment terms with prior sentiments assigned to them. The second was, to a lesser extent, the ignoring of irrealis and modal factors in text. By testing the tool's performance on sample documents from the domain of interest repeatedly over two iterations, the dictionary has also been improved. Though we expected a larger number of modifications in the original English dictionary, there were not many that were eventually made. The results clearly establish the feasibility of the tool to perform well to detect negative sentiment in domain specific documents. This would enable us to detect presence of issues by using sentiment analysis as the method of choice.

Future Work

The research reported in this paper can be subjected to many improvements. As discussed in Section 5.2, the major improvement that is immediately possible is to assign finely tuned values of sentiment priors to words in the dictionaries. The number of entries in the dictionary will have to increase once more documents are studied, and might reach a steady state once a large number of documents have been studied. From a larger perspective, an important part would be to have target specific sentiment lexicon, and a means to use it. The sentiment value of a word may be of two types -prior, or context (target) dependant. We have currently addressed only the prior values in the aircraft assembly domain. As seen in the example of "cold pizza" vs "cold coke" by Fahrni and Klenner [START_REF] Fahrni | Old wine or warm beer: Target-specific sentiment analysis of adjectives[END_REF], it is necessary to associate specific words which are context (target) sensitive. Although there were no concrete cases in our test examples which suffered because of this, we can foresee that this may well be a problem (e.g. "blind rivets" is not negative, but "blind spot" is).

The other issue, seen in some cases, is that of ambiguity of word sense (By "late" autumn...). This is a commonly occurring issue during processing of natural language text, and methods like Word Sense Disambiguation (WSD) are suggested as means to resolve it.

From the perspective of creating the domain specific sentiment dictionary, creating a lexicon in a manual way is usually subjective and might be prone to errors. Automatic methods, many of which have also been described in literature here, may be used to improve the size and quality of the dictionary, since it remains to be seen how the size of the dictionary would grow over larger numbers of documents.

Table 1 .

 1 Initial performance of the tool, without adding any specific domain lexicon.

			Classification by SA tool
			Positive	Negative
	Classification	Positive	144 (TP)	20 (FN)
	by researchers	Negative	68 (FP)	101 (TN)

Table 2 .

 2 Effects of enhanced dictionary (first, second iteration) and changed settings(second

	iteration)
	Iteration	TP	TN	FP	FN	Others
	Zero	144	101	68	20	24
	First	140	112	57	24	24
	Second	145	138	31	19	24

Acknowledgments. The authors are very grateful to Prof. Maite Taboada of the Simon Fraser University for making available the SO-CAL tool and the in-built dictionaries of SO-CAL for our research purposes, since the tool is the foundation for building the sentiment analysis work. This project was carried out with funding from The Boeing Company USA, under SID Project PC 36030.


Vishal Singh
email: vishal.singh@aalto.fi

BIM ecosystem research: What, why and how? Framing the directions for a holistic view of BIM

Keywords: BIM, ecosystem, coevolution, impact analysis, research methods

This paper presents theoretical arguments for BIM ecosystem research, based on concepts of coevolution and emergence in a complex network of constituent elements: products, processes, people, technology and policies. Broader trends such as Internet of Things, data analytics, and digital manufacturing are increasingly becoming integral to BIM systems. Consequently BIM research is evolving along multiple pathways. Therefore, there is need for methodologies and approaches to analyze BIM ecosystem. Three types of ecosystem analyses are proposed: (1) BIM-Ecosystem Retrospective Analysis (BIM-ERA), to understand the ecosystem's constituents and their dependencies, based on facts and history, (2) BIM-Ecosystem Feasibility analysis (BIM-EFA), to be able to use the knowledge of the constituents and their dependencies to assess whether an intended intervention in a given ecosystem is feasible or not, and (3) BIM-Ecosystem Impact Analysis (BIM-EIA), to be able to conduct whatif studies to assess the potential short-term and long-term impact of a potential action.

Introduction

Building Information Modelling (BIM) has gained global attention across the industry, academia and the policy makers. This is fueling rapid growth of the BIM ecosystem, which, following [START_REF] Moore | The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems[END_REF], has been defined as the network of interacting technologies, processes, policies and organizations that collectively determine the development and evolution of BIM related products and services [START_REF] Gu | BIM Ecosystem: The Coevolution of Products, Processes, and People[END_REF]. For example, BIM adoption in the US grew from 17% in 2007 to a remarkable 70% in 2012 [START_REF] Bernstein | The Business Value of BIM in North America: Multi-year Trend Analysis and User Ratings (2007-2012)[END_REF]. During the same period several new BIM products and services have emerged in the market, while government agencies across countries such as the UK have created national BIM guidelines and policies. In such a dynamically evolving BIM ecosystem there is limited understanding of how the BIM technologies, processes, policies and organizations are mutually coevolving. These developments have opened a new multi-disciplinary research frontier around BIM, with long term implications for theory, education and practice.

In the recent years, several streams of BIM research have emerged across specific issues such as BIM adoption, product data models and standards, facilities management, and collaboration [START_REF] Yalcinkaya | Patterns and trends in Building Information Modeling (BIM) research: A Latent Semantic Analysis[END_REF]. However, the lack of systemic understanding of the BIM ecosystem is a critical knowledge gap that needs attention. First, since BIM is viewed as a collaborative approach, the potential benefits of BIM are contingent on how the technologies, processes, policies and the organizations fit together e.g. [START_REF] Singh | A theoretical framework for BIM-based multi-disciplinary collaboration platform[END_REF]. Second, a comprehensive understanding of the systemic challenges is critical to facilitating effective BIM strategy and execution. Third, the emerging BIM research discipline requires strategic insights and directions for future research.

Therefore, this position paper aims to highlight the need for a theoretical basis to understand the patterns and implications of the coevolution in BIM ecosystem at the micro (e.g project and organization) and macro levels (e.g industry and society). A better understanding of the BIM ecosystem will improve decision making and collective growth for various stakeholders who are directly or indirectly part of the BIM ecosystem. For example, at the macro level, what are opportunities and challenges for different stakeholders in the evolving BIM ecosystem? What role can the government agencies, policy makers and regulations play in fostering a BIM ecosystem that is conducive to economic, environmental and social welfare? At the micro level, what are the dependencies across emerging technologies, processes and organizations that we need to know for effective management and implementation of BIM-based projects? Similarly, how are the macro and micro levels of BIM ecosystem mutually related? And, how are the broader technological trends such as internet of things (IoT) and digital manufacturing likely to affect the BIM ecosystem?

In addition, there are several fundamental conceptual issues that need to be resolved. For example, how do we define the boundary of the BIM ecosystem? Which technologies, which roles, what processes, and what policies, direct or indirect, should be considered part of the BIM ecosystem and which need not? Or a more fundamental question is whether we at all need to define the boundary? What are the theoretical and conceptual implications of the chosen approach and how could that impact BIM research and development, as well as decision making in practice?

More recently, a systemic view of BIM is starting to emerge in the discussions such as BIM research frameworks e.g. [START_REF] Succar | Building information modelling framework: A research and delivery foundation for industry stakeholders[END_REF], BIM execution plans e.g. [START_REF] Messner | BIM Project Execution Planning Guide[END_REF], and maturity matrices e.g. [START_REF] Succar | Macro-BIM adoption: Conceptual structures[END_REF]. Along with these, a comprehensive understanding of BIM ecosystem is required, especially from the viewpoint of co-evolutionary mechanisms and dependencies across different levels of granularity, micro to macro. An endeavour to understand the systemic change in construction is not new. Previous attempts at forming a systemic view of the development of construction IT have typically taken the form of industry forecasting, review articles, policy documents, strategy reports and roadmaps e.g. [START_REF] Rezgui | Paving the Way to the Vision of Digital Construction: A Strategic Roadmap[END_REF][START_REF] Kim | Developing a technology roadmap for construction R&D through interdisciplinary research efforts[END_REF]. Therefore, one of the key objectives of this paper is also to clarify how the proposed BIM ecosystem perspective is different to such approaches, and yet how the ecosystem perspective can also contribute to the development of maturity models, creating roadmaps and identifying strategic directions for research and practice.

2

BIM Ecosystem: What should we aim to research and why?

2.1

Identifying the key constituents of the BIM ecosystem.

The first steps is to identify the key constituents and categories, such that the key elements and their position in the ecosystem can be located. This iterative identification, mapping and clustering of the elements is a longitudinal process because the ecosystem continues to evolve. Such an effort will require a methodological approach, grounded in sound-theoretical and conceptual basis. An ontology to describe the BIM ecosystem is desirable. The ontological approach can build on related work in the literature. For example, [START_REF] Succar | Macro-BIM adoption: Conceptual structures[END_REF] have described eight key components of a BIM maturity model, in the context of macro-level BIM adoption. A critical review of the different approaches to classifying and clustering the constituent elements of the BIM ecosystem is needed to assess their strengths and limitations. There is conspicuous absence of any methodological analysis or critical review of these classifications in the BIM literature. Critical questions need to be discussed and debated. For example, what are the structural characteristics of the ecosystem: is there a structural pattern at all? Is it hierarchical and/or nested and/or modular? What are the levels of details to consider? How do we ascertain the parity of the different concepts chosen for each level? etc.

Identifying dependencies and rules of interaction between the elements

The dependencies between the constituent elements need to be identified [START_REF] Peters | Cross-scale interactions, nonlinearities, and forecasting catastrophic events[END_REF][START_REF] Rammel | Managing complex adaptive systems-a coevolutionary perspective on natural resource management[END_REF]. These dependencies influence the patters of emergence, which can be traced from the historical data, but at the same time these dependencies may influence the emergent patterns of the future. While mapping the dependencies between these elements, once again several aspects need to be considered. For example, which relationships are constant and which of these are variable? What are the factors influencing the variable relationships, for e.g, temporality, regionality, etc? What are global and local variations in the dependencies? How can the dependencies be defined?

Developing methodologies for BIM ecosystem analysis

Besides knowing the constituent elements of the BIM ecosystem, its structural characteristics, and the dependencies between the elements, we need methodologies to analyze the dynamic patterns of evolution-to understand how changes emanate and propagate through the ecosystem-to assess aspects such as stability, robustness and centrality of the ecosystem, as found in any complex network e.g. [START_REF] Strogatz | Exploring complex networks[END_REF]. The methodological approaches can broadly focus on the following aspects:

BIM ecosystem retrospective analysis (BIM-ERA). The retrospective analysis is based on the past, and hence, there are historical data, facts and experiences to build an understanding of the BIM ecosystem. While the future evolution may or may not be dependent of the past events, lessons learnt from the past, and dependencies and patterns observed in the past, may provide useful insights into the future trends.

BIM ecosystem feasibility analysis (BIM-EFA).

Once a mapping of the BIM ecosystem begins to emerge, it is desirable to be able to make informed decisions about potential actions that can be taken to bring about a desired change in the ecosystem. The feasibility analysis methods should allow assessing whether a potential action or solution is possible within the given ecosystem or not, that is, is the action or solution feasible? Constraints within the environment or dependencies with other potential actions or problems may render a solution non-feasible in the context. Such an assessment should also give insights into potential barriers to feasibility.

BIM ecosystem impact analysis (BIM-EIA

). An action or solution that is feasible may have varied short-term and long-term impact on the ecosystem, some of which may be desirable and some may not. Thus, it is not sufficient to analyze the feasibility, but it is equally important to analyze the impact of the action or solution. The concept of BIM-EIA is inspired by approaches such as Environmental Impact Assessment e.g. [START_REF] Stewart-Oaten | Environmental Impact Assessment: Pseudoreplication in Time?[END_REF] commonly used in ecological domains, where systemic changes or likely change propagation and impact is thoroughly studied before a major action or solution is approved. This aspect has so far been entirely overlooked in the study of ICT or business ecosystems, both within and outside BIM research. Nonetheless, impact analysis should be considered for the following reasons: Some actions or solutions may have high initial investment both in terms of cost, time and effort, which means they require systemic effort for implementation and execution over a longitudinal period of time. Such efforts can only be made occasionally. For example, in the context of BIM systems if an organization makes an investment to adopt new BIM applications across the teams, then the cost of the applications, training the staff, and beginning to use it effectively is a high investment decision. Once that has been committed to and reached mid or advanced stage, then moving to another set of applications across the teams in a short-term, even if they may be better than the chosen solution, will become extremely challenging. Some actions or solutions may be irreversible in nature. For example, organizations may invest in building systemic processes and culture to support certain technologies and approaches. Once these actions and steps have been taken they cannot be undone, and more systemic effort will be required to replace them if they do not provide the intended outcomes, or worse, lead to undesirable outcomes.

While some actions or solutions may require one-off effort, other actions or solutions may require continuous effort over a longer period of time. It is important to assess whether continuous effort is sustainable or not.

In considering impact analysis, it is also important to define a benchmark to identify what is the alterative? That is, while it is important to assess what may be the impact of an action, it is also important to assess what will happen if the action is not taken? Is there an alternative action, and how does it compare? Contingent on the type of action or solution, the future course of actions such as flexibility, adaptability, scalability, etc can be severely impacted.

3

How is the proposed BIM ecosystem approach different?

This section briefly discusses how the proposed approach is different to the existing approaches in BIM research and practice from a systemic point of view:

Normative models of BIM research versus co-evolutionary view of BIM ecosystem.

Most of the current approaches to present a systemic view around BIM are descriptive and normative. Typically, these approaches outline sequential and linear stages and directions of BIM development [START_REF]BIS/Industry Working Group: Building Information Modelling (BIM) Working Party Strategy Paper[END_REF]. For example, BIM-levels 0-3 have been defined, as progressive and sequential levels. While such clear definitions of the levels are useful in determining the maturity levels and creating a roadmap, it can be argued that such an approach fails to sufficiently account for emergent trends and directions of development within the context of BIM research. For example, parallel developments such as laser scanning, internet of things (IoT), etc are being integrated into the BIM systems, but these cannot be directly straightjacketed into the current definitions of these pre-defined levels, which are limiting, and close-ended. In contrast, the ecosystem approach emphasizes a co-evolutionary view, whereby the constituent elements are mutually evolving. At the same time, it starts with a premise that the boundary of the ecosystem is fuzzy and ill-defined. Theoretically, the co-evolutionary nature and the fuzzy boundary of the ecosystem means that we do not yet understand the lifespan of the BIM ecosystem, and nor do we know where all, in which directions, and by how much, the BIM ecosystem could grow. Consequently, while assessing the maturity in BIM research is useful, the maturity models need to be revised every once in a while and the limitations of defining the maturity level at any given time need to be outlined, because the reference lifespan and scope of BIM research is inadequately understood.

The concept BIM ecosystem impact analysis or any equivalent concept does not exist.

The existing approaches have (1) somewhat focused on understanding and mapping the constituent elements of what we describe as the BIM ecosystem, and the dependencies between them, i.e., kind of retrospective analysis, or (2) somewhat focused on understanding how to bring about a desired change within the context of the BIM ecosystem, i.e., kind of feasibility analysis. For example, developing mapping frameworks, maturity models, creating roadmaps, forecast models, etc fall in this category, with the objective to try and understand what is out there, and what can be done in the future to reach a desired state. However, the concept of impact analysis has not been considered at all, and there has been no discussion on whether the short-term or long-term implications of decisions taken in the current context can be or should be evaluated upfront. The objective of such impact analysis is to reinforce and validate the suitability of the feasible actions and solutions in the larger social, economic and environmental context, as well as to prepare in advance to address likely unintended effects of an action or solution.

Some of the other key factors that we propose to consider while developing an understanding of the BIM ecosystem, and which need clarification include:

Distinguishing between deterministic, predictive, and exploratory preparations.

Developing models of a complex system with the view to try and understand likely future trends is often viewed with skepticism, because a number of forecast models and roadmaps have failed to make any meaningful contribution and they have been way off the mark. Therefore, it is important to clearly outline the purpose and expectations from a BIM ecosystem model. Rather than trying to build a deterministic or predictive model that can forecast how the BIM ecosystem will evolve, the BIM ecosystem research should allow exploration of various what-if scenarios that indicate numerous potential paths of evolution of the BIM ecosystem, given different conditions and emergent situations. It is expected that a what-if study will allow the ecosystem stakeholders to be better prepared for the likely scenarios than otherwise.

Multiplex networks. The use of social network analysis and similar methods to understand the dynamics of construction sector is not new. Several aspects of construction sector, ranging from teams and project management to understanding the industry level interaction have been studied using the network approach. Nonetheless, given the complexity of network research and the challenges in mapping the network dependencies, the typical research is limited to one view of the network, whereas the actors within a given network may have different dependencies within the same network, or may be part of multiple networks at the same time, which all might influence their decisions and actions in any given network. This is typically overlooked because multiple networks are not juxtaposed and investigated at the same time. Therefore, the multiplex networks approach [START_REF] Kim | Coevolution and Correlated Multiplexity in Multiplex Networks[END_REF] needs to be considered in developing a comprehensive understanding of the BIM ecosystem.

BIM is beyond tools and processes. Following considerable debate on whether BIM is merely a tool or a process, there is an increasing acceptance that BIM is a set of tools, process and policies. Nonetheless, if the research in BIM needs to be advanced it is desirable to look beyond this view as well. For example, the evolution of BIM has many similarities with Management Information Systems (MIS), where similar debates ensued for a while. Eventually MIS emerged as a formal discipline in itself with several notable theoretical perspectives associated with MIS research in areas such as cognition [START_REF] Vessey | Cognitive fit: An empirical study of information acquisition[END_REF], media [START_REF] Daft | Information richness: a new approach to managerial behavior and organizational design[END_REF], social science [START_REF] Mumford | Sociotechnical Systems Design: Evolving Theory and Practice, Manchester Business School[END_REF], and behaviour [START_REF] Venkatesh | User Acceptance of Information Technology: Toward a Unified View[END_REF]. In contrast, there has been limited theory building attempts in BIM research. Thus, it is argued that analogous to MIS we also need to view BIM as a discipline where fundamental theoretical contributions can be made both within and across multi-disciplinary boundaries.

Building a comprehensive view of the BIM ecosystem

This section reviews some of the complementary research methods and approaches that can be used to understand and analyze the BIM ecosystem.

Empirical studies. Qualitative empirical research based on case studies, surveys, workshops, interviews and similar data collection techniques is the most dominant research method in current research on building a holistic view of BIM. Typically the findings are used to identify various (success) factors associated with BIM and how they impact adoption, maturity, etc. Many of these studies focus on micro-ecosystem, especially based on case projects and feedback from the actors involved in the project.

Other studies focused on macro-ecosystem tend to include workshops, surveys and focus groups to assess the status of BIM adoption, perception of actors and different stakeholders, role of standards, etc. However, barring a few notable articles, there is limited attempt to build a theory of BIM, and most articles report observations and findings without much articulation towards theory building.

Delphi studies. Delphi methods are based on systemic engagement and interviews with a panel of experts. Interviews are conducted in multiple iterative rounds, separately with each member of the panel. It is expected that the expert opinions of the BIM ecosystem will converge with each round. The success of Delphi studies is contingent on how quality of the chosen panel and how representative the panel is of the various facets of the BIM ecosystem.

Literature review. Literature review can be qualitative and subjective, which is typically the case, or they can be objective and quantitative, based on computational methods that can also analyze large data sets e.g. [START_REF] Yalcinkaya | Patterns and trends in Building Information Modeling (BIM) research: A Latent Semantic Analysis[END_REF].

Data mining and analytics. With data mining and analytics, e.g. Big Data approach, it should be possible to conduct a comprehensive retrospective analysis, BIM-ERA, to assess and understand how the BIM ecosystem has evolved. The data-driven, fact-based findings can complement the views and opinions of experts about the evolution of BIM ecosystem, including those in Delphi Studies. The data analytics approach can be applied at both macro and micro levels. At macro levels, the trends and patterns can be studied at the industry level, while at the micro-level the large amount of data generated during a project or several projects can be analyzed to identify the patterns and dependencies at the project level. Furthermore, at the project level there is also a possibility now to mine all the BIM data generated during the project, and such as an analysis could also give insights into design decision making and associated dependencies.

Complex systems and networks. BIM ecosystems can be viewed as complex systems and networks. The complex systems approach has been applied by researchers in understanding construction projects as well the construction sector. The different related research methodologies can also be applied to the understanding of the BIM ecosystem. Some of relevant methodologies could include network analysis, mathematical and stochastic modeling, and simulations.

Simulation-based what-if studies. One of the objectives of trying to understand and build a theory of the BIM ecosystem is to be able to assess the patterns of evolution, and be able to conduct feasibility analysis as well as impact analysis. The challenges in conducting feasibility analysis and impact analysis is to be able trace the various dependencies and their consequences over a longitudinal chain. That is, an undesirable potential impact may be associated with a factor further down the value chain, and not necessarily easy to trace without a thorough analysis. As an analogy, a single move in a game of chess may be detrimental further down the game, but may not be obvious in the immediate few steps. The implications of this step is not independent of how the next few steps pan out, and how the opponent responds to the steps taken by the first player. These sequence of steps are typically non-deterministic, and hence, various possible paths of evolution are possible. Computational simulations based on probabilistic models provide a powerful method to conduct what-if studies, and these methods have been used successfully in various fields of complex systems research. As discussed in this section, there are multiple research methods that can be used to study the BIM ecosystem, and the different methods can mutually complement and reinforce the understanding of the ecosystem, Figure 1.

Fig. 1. Research methods applicable to different aspects of BIM ecosystem anaylsis

Conclusions

This paper argues the need for a BIM ecosystem perspective, based on the concepts of co-evolution and emergence. The paper argues that barring a handful of notable articles there has been limited effort at building a holistic and theoretical view of BIM research environment. Furthermore, the typical approach in developing a systemic view of BIM research and development has been descriptive or normative, giving guidelines and defining levels of maturity, with limited space to account for emergent factors. The paper argues that the normative models of BIM research are limiting because they assume closed boundaries and scope of BIM. In contrast, the broader socio-technical trends such as building automation systems, IoT, big data analytics, etc are also finding their way into the mainstream BIM research and BIM systems. Consequently, discussions on maturity models and BIM levels appear hasty and misplaced until the BIM ecosystem boundaries are established with greater research rigour. Considering the co-evolutionary nature of the constituents of the BIM ecosystem, the definition of the BIM ecosystem boundary remains a challenging tasks.

Therefore, this research emphasizes the need for theoretical and methodological approaches to support BIM ecosystem analysis based on the underlying dependencies, patterns and mechanisms of co-evolution. Three different aspects of ecosystem analysis are differentiated (1) BIM-ERA that uses varied methods including qualitative studies, Delphi studies, literature review and big data analytics to review how the BIM ecosystem has evolved, based on the experience so far, (2) BIM-EFA that uses the understanding of the BIM ecosystem built from BIM-ERA, together with Delphi studies, literature review and what-if simulations based on complex systems modelling to assess the feasibility of desired actions in a given ecosystem, and (3) BIM-EIA that also uses the understanding of the BIM ecosystem built from BIM-ERA, together with Delphi studies, literature review and what-if simulations based on complex systems modelling to assess the short-term and long-term impact of a potential feasible action. One of key contributions of this paper is to highlight the need for a BIM-EIA framework, which has not been considered at all in the current BIM literature.

In addition, it must be noted that most of the current articles directed towards a holistic view of BIM research are based on expert opinions, theoretical arguments or other qualitative studies, but there is very little use of complementary methods such as complex systems modelling, what-if simulations or data analytics. It can be argued that the ability to compute emergent patterns of evolution and emergence over longitudinal patterns is limited in such qualitative approaches, while computational methods can be used to explore unlimited number of potential paths of evolution for extended periods of time, under varied what-if conditions. The usability of such computational models are dependent on how well we understand the underlying mechanisms and patterns in BIM ecosystem, and how well we are able to translate them into computational models where complex patterns emerge at global levels, based on well-established dependencies and rules of local interactions between constituent elements.

In summary, this paper argues the need for an ecosystem view of BIM research, based on diverse theoretical and methodological approaches. The paper is a first attempt at articulating the directions and approaches that are required to create such a systemic view, and this remains the primary limitation of the paper. Such an attempt requires collective debate, review and iterations, and one of the main purposes of this paper is exactly to initiate such a theoretical and methodological culture in BIM research..


Patrick Mbassegue
email: patrick.mbassegue@polymtl.ca

Ma-Lorena Escandon-Quintanilla
email: lorena.escandon-quintanilla.1@etsmtl.net

Mickaël Gardoni
email: mickael.gardoni@etsmtl.ca

Knowledge Management and Big Data: opportunities and challenges for small and medium enterprises (SME)

Keywords: knowledge management, big data, data analytics, SME, small medium enterprises

Whenever a new technology is made available, it is worth finding possible uses in different domains. The advancement of information technologies now enables a wealth of information to be digitally collected and exploited for knowledge management and reused for further knowledge creation. Big data enables organizations to capitalize on large amounts of data by bringing together different sources of information to find trends and knowledge that are only visible with large amounts of data. This knowledge can then be applied for knowledge exploration, exploitation and decision making.

1-Introduction

Knowledge management allows companies to make the most out of the information they collect and generate. However, it relies on an ongoing effort by companies to keep their records, and process them continuously. It requires dedicated resources to explore the information generated outside organizational borders to remain competitive and be able to react to environmental changes.

New information technologies now make it possible to build a knowledge based which can include internal and external information, and provide insights and knowledge which can only be detected when large amounts of data are being studied.

In this paper, we discuss the link between big data tools and techniques and how they can support a knowledge management effort in a small or medium enterprise, as well as the challenges they might face when undergoing such efforts.

2-Big Data and managerial implications 2-1 The concept of Big Data

Big data stems from our current ability to generate exponential volumes of data. Data is everywhere and growing at an exponential rate [START_REF] Lynch | Big Data: How Do Your Data Grow?[END_REF] [START_REF] Lynch | Big Data: How Do Your Data Grow?[END_REF]; Tweed, 2013 [START_REF] Tweed | Smart Meters Deliver 1 Billion Data Points Daily[END_REF]; Delort, [START_REF] Delort | Le big data. Collection Que sais-je?[END_REF]). It comes from individuals, groups, organizations, networks, connected objects, etc. (McKinsey, 2011 [4]). However, it is necessary to find ways to make data more accessible (Dove & Jones, 2014 [START_REF] Dove | Using Information Visualization to Support Creativity in Service Design Workshops Proc Service Design and Innovation Conference[END_REF]).

The current threshold of a big data set is about an exabyte, or a quintillion bytes" (Howkins, [START_REF] Howkins | The creative economy: How people make money from ideas[END_REF]). Nonetheless, [START_REF] Howkins | The creative economy: How people make money from ideas[END_REF] [START_REF] Howkins | The creative economy: How people make money from ideas[END_REF] explains that the criteria for big data is not only the size, but the variety in the data, the potential relationships between the data and the need for new tools to be able to exploit the data (see also Maniyka et al., 2001 in Kabir & Carayannis 2013) [START_REF] Kabir | Big Data, Tacit Knowledge and Organizational Competitiveness[END_REF]. Because data can be reused and analyzed in different ways to find new insights, companies need to take advantage of the wealth of data by using it in a more creative fashion [START_REF] Howkins | The creative economy: How people make money from ideas[END_REF] [START_REF] Howkins | The creative economy: How people make money from ideas[END_REF].

The produced data is distinguished by a number of characteristics, also known as the 5Vs: volume, variety, velocity, veracity and value (Laney, 2001 [8]; Koutroumpis & Leiponer, 2013 [9]). The combination of these characteristics shows the richness they entail for organizations, especially for decision making.

The data also allows for a more precise understanding of phenomena and the discovery of relationships that exist between variables; to not only identify the linear causalities, but also the cross-effects between the variables involved in an observed phenomenon (Beyer & Laney, 2012 [10]). Indeed, we not only try to understand what happens at time t, but also to identify the trend and what will materialize at t+1. The predictive dimension, and consequently an ability to manage uncertainty, is sought: what is beneficial to the manager to successfully accomplish the primary tasks of management: plan, organize, manage, and control (POMC).

2-2 Big data needs SMEs

For organizations, the presence of this much data represents a challenge, as well as potential new opportunities (George and al., 2014 [11]). Consequently, SMEs face the same challenges and issues as large organizations concerning the need for information, to process it, and use it to make decisions. In this sense, SMEs are subject to a large volume of internal data (which they generate and / or collect) just as much as external (data produced in their specific industry, as well as for the overall business ecosystem). The data affecting SMEs is increasingly large; they are collected in real time from various sources (Liebowitz, 2013 [12]; Tweed, 2013 [START_REF] Tweed | Smart Meters Deliver 1 Billion Data Points Daily[END_REF]). For SMEs, the imperatives of Big data are situated in 3 levels: internal environment, external environment and organizational performance.

Internally, SMEs are structured in different departments or functions, each of which generate and collect important volumes of data, but different in nature. For example, the data generated and collected in marketing department is different from the data in production, finance, human resources or supply management. All these data are necessary and essential to generate value (Delport, 2015 [START_REF] Delort | Le big data. Collection Que sais-je?[END_REF]). SMEs need an infrastructure adapted to optimally exploit its data; in that way Big data attributes can reconcile all these disparate data and enable their processing on a common basis. It enables the optimization of exploitation activities.

The daily management practices show that the diversity of functions and data induces a managerial structural division which has a negative impact on the quality of decisions. However, the functions that make up the organizational structure are the basis and the architecture that enable SMEs to offer products and services customers expect.

Externally, SMEs are found in an environment that creates pressures to which they must develop adaptation strategies and solutions. However, this environment produces an exponential amount of data on different levels based on economic, political, competitive, technological, social and demographic variables (McKinsey, 2011 [4]; George and al., 2014[11]). To ensure its survival and especially improve its competitive position, SMEs must collect, process and integrate external data in decision making.

The market competition has expanded and is not limited by geographic boundaries; therefore, ignoring it will contribute to weaken the basis on which decisions are taken by SMEs. Moreover, the consideration of a large and diverse volume of data enables a better understanding of customers, the quality of the service provided, and its relevance to each of the specific groups of consumers. Thus, based on a critical mass of diverse data, external to the organization, SMEs can better understand its ecosystem, environment and offer products and services that serve its various market segments (Chan, 2014 [13]; George and al., 2014 [11]).

Based on these environmental constraints, SMEs need to consider all the data at their disposal, both to optimize the foundation of its operations and the combination of its internal resources to improve its external position and its ability to provide the answers needed by its clients.

3-Knowledge management and managerial implications 3-1 Knowledge management concept and activities

The concept of KM or knowledge management is subject of different representations, particularly among sociologists (Latour, 1987 [14]), philosophers of science (Polanyi, 1966 [15]), economists (Winter & Nelson, 1982 [16]), industrial economists (Porter, 1987 [17]), promoters of knowledge engineering (Tarondeau, 1998 [18]; Ermine, 2000 [START_REF] Ermine | Les systèmes de connaissance. 2ème édition[END_REF]) and strategic management (Wernerfelt, 1984 [START_REF] Wenerfelt | A Resource-Based View of The Firm[END_REF]; Barney, 1991 [21]; Wright & Van Wijk, 1995 [22]). Despite these differences, it appears that knowledge becomes a source of income that allows a firm to stand out in its market or markets. Therefore, knowledge prevails as much at individual and collective levels (service, department, division) as at an organizational level (Lebowitz, 1999 [START_REF]Knowledge Management Handbook[END_REF]; Alavi & Leidner, 2001 [START_REF] Alavi | Knowledge Management and Knowledge Management Systems : Conceptual Foundations and Research Issues[END_REF]).

Knowledge management activities correspond to different organizational aspects, namely managerial practices, material and technical structures that enable the identification, processing and dissemination of knowledge (Veybel & Prieur, 2003 [25]; Ling, 2011 [START_REF] Ling | Defining Knowledge Management Activities from Information Communication Technologies (ICTs) Perspective[END_REF]). In this case, we are talking more of a global knowledge management that takes into account different aspects namely: 1-The implementation process of activities, material and organizational infrastructure; 2-From which the players in the organization develop collaborative relationships, partnerships and exchanges; 3-The development, coding, creation, acquisition, storage, processing and use of knowledge; 4-By relying on human relations and organizational support, relationship, material and technology.

The overall management of knowledge should lead to a situation that favors organizational performance. The latter is generated by the emergence of a new culture and practices based on the exploitation of organizational knowledge. KM also allows the organization to eventually build a competitive advantage and improve or maintain its competitive position.

Managers then have the mandate to build the organizational capacity to develop a competitive advantage that is materialized by a better competitive position. Knowledge thus becomes a matter of strategic interest. The resources mobilized by the organization must allow the creation of value in a context of increasingly competitive markets. To do this, managers must seek different ways to promote the creation of value; it is necessary to master and apply individual and collective intelligence based on the combination of available knowledge. KM is therefore one of the critical ingredients to create value, especially in today's economy which is increasingly intangible and service based.

3-2 Knowledge management and needs for SMEs

KM is recognized by many as a framework of choice for organizations to create value: What about small and medium-sized enterprises (SMEs)? SMEs, beyond their relative weight in the ecosystem, are characterized by other dimensions. They are generally small in size and concentrated in specific market segments (Marchesnay and al., 1990 [27]). Their income depends on a limited number of market segments, products or services, makes them especially vulnerable. There are less diversified than large organizations (Julien, 2002 [28]). All these dimensions weaken the SME. Furthermore, management is often concentrated in the hands of the founder who also serves as senior manager or CEO. The CEO not only has the authority because he or she controls the capital, but also the legitimacy of being the founder (Julien, 2005 [28]; OCDE, 2005 [START_REF]OCDE : Perspectives de l'OCDE sur les PME et l'entrepreneuriat[END_REF]). Because of its position and its multi-dimensional role, the place of the founder is critical. The success and performance of the SME will depend on his/her potential, abilities and knowledge.

In terms of management practices, it appears that the strategic fragility of the SME is also characterized by the practice of solving problems day to day without relying systematically on previous experiences (Asmaa, 2010 [30]). Thus, the accumulated experiences held by the few employees who are part of the SME may not be taken into account.

In the SME, all structural features related to production or service delivery system predominate. But what characterizes the organizational performance is the structural balance between structure strategy and the ability of leaders to take into account the dynamics of the environment by offering answers (products or services) expected by potential customers. Indeed, based on the findings relating to the SME management practices, KM is an indispensable framework to be effective and efficient. Effectiveness is induced by KM to the extent that identification activities will help better identify the needs of clients. Efficiency is induced by the capitalization of both internal and external knowledge. The capitalization of knowledge contributes to the optimization of the resources mobilized to provide products and services. This optimization of resources also has an impact on the costs of goods and services, because based on the capitalization and enhancement of knowledge it is possible to find innovative solutions to improve quality and productivity (Mbassegue and al., 2015 [31]).

KM for the SME is critical because it will help adopt management practices that will allow combining all critical resources, namely its processes and knowledge. Building a collective and organizational intelligence, permitted because of KM, promotes the emergence of shared practices of assimilated knowledge, mastered and produced by all actors of the SME.

4-The basis of knowledge management and big data integration 4-1 Association of knowledge management and big data: strategic and operational necessity

Starting with Nonaka and Takeuchi (1995) [START_REF] Nonaka | The Knowledge Creating Company: How Japanese Companies Create The Dynamics of Innovation[END_REF], the domain of Knowledge Management has been engaged in researching the way knowledge is created and transmitted within companies. This domain evolved to include the adoption of computer systems to support different aspects of KM. KM is at the service of the organization, particularly for value creation. In this sense, it's important to characterize knowledge management activities and big date dimensions to find a common base for integration. On one side, knowledge activities means identify, capture, store, analyze, disseminate, share and develop new solutions and innovative solutions which contribute to this creation of value. On the other side the characterization of Big Data is established from 5 dimensions (volume, velocity, variety, veracity, value), which are necessary to the creation of value (Laney, 2001 [8]).

The combination of KM and Big data is necessary because of the reality of SME management and the inputs for basic value creation. The base of KM is about to manage knowledge. Therefore, knowledge cycle is characterized by three elements: the data, which when contextualized becomes information, and in turn information becomes knowledge when processed. Data is therefore the first material from which we arrive at knowledge of different kinds, following the application of various transformational knowledge (algorithms, artificial intelligence base, etc.) recognized as the basis of analysis (or analytics) and treatments made with the support of information technologies (Sathi, 2012 [33]; Minelli and al., 2013 [34]). The achieved knowledge is usable, structured and, mostly, generates value for the organization. This whole cycle embodied elements from Big data characteristics and dimensions relating to KM activities which in fine contributes to the global performance of the SME (Figure 1).

Conceptually, the model of the processing chain (income, input, throughput, output, outcome) is as relevant in situations of KM as Big Data. Indeed, this conceptual model helps to understand how the SME identifies needs it wants to respond, what resources to mobilize and the products and services to offer in this regard. For this model to materialize in an effective performance, the data collected must be important, varied, voluminous, and collected in a systematic manner. The SME will develop effective processing capacity of inputs into outputs, with the purpose of creating value. Including large quantities of data nourishes the process level activities: that which characterizes the nature of KM as well as Big Data (Bose, 2009 [35]).

Moreover, whether at the beginning of the organizational value chain model (income, input), for processing (process), or at the outlet (ouput, outcome), the need for large quantities and varied data is necessary so managers can take on the challenge that is theirs, namely achieving arbitrations that will lead to the optimal allocation of scarce resources, and to create value. This is the real result sought by the SME when implementing initiatives combining KM and big data (Rothberg & Erickson [START_REF] Rothberg | From Knowledge to Intelligence: Creating Competitive Advantage in The Next Economy[END_REF], 2005; Sathi, 2012 [START_REF] Sathi | Big Data Analytics: Disruptive Technologies for Changing The Game[END_REF]).

Baesens (2014) [START_REF] Baesens | Analytics in a Big Data World[END_REF] underlines others aspects of the link between knowledge management and big data. Figure 2 depicts this analytics process. In this model, the first step is the gathering of data from different sources, followed by the data selection, cleaning and transformation, to provide users with the data analyzed in patterns. It is up to the users to obtain insights from the patterns.

Fig. 2. The analytics process model [START_REF] Baesens | Analytics in a Big Data World[END_REF] In sum, these dimensions are functionally nested in each other. Data mining, processing and transformation will lead to the discover patterns and structures that can make sense of the data. Because there are different types of data, a greater volume means more matches, which will rich the potential application and exploitation (Bose, 2009 [35]; Erickson & Rotheberg, 2014 [START_REF] Erickson | Big Data and Knowledge Management: Establishing A Conceptual Foundation[END_REF]). To succeed in this operation and the extraction of value in the data, it takes knowledge or prior knowledge. Big data provides the necessary variety and especially appropriate analytical tools to make a more efficient organization by setting in place an initiative to treat and exploit diverse data.

4-2 Potential benefits of the knowledge management and big data integration

Benefits stemming from the combination of KM and Big data can be assigned to three key activities: knowledge exploitation, knowledge exploration and decision making. We talk about exploitation when we discuss the efforts in an organization to take advantage of internal resources to improve the solutions offered. In using KM to exploit internal resources, the effort of linking it with Big data could result in a better understanding of the current market segment. In turn, this understanding can be applied to:

-Improve current users' satisfaction -Build a knowledge base from previous product development efforts Knowledge Exploration refers to the activities performed by an organization to find out about knowledge outside the frontiers of the company. It means keeping track of competitors, news, research and trends that might affect their operations. By including external data sources as an input for the organizational effort of combining KM and Big data, the insights of this exploration can lead to:

-The identification of potential new products / services -The identification of complementary resources (opportunities for collaboration, new suppliers)

To make decisions, organizational leaders need to rely on different variables (sales, income, number of customers, customer geography, market segments, employee skills and productivity, etc.). In practice, however, managers are limited to a few variables in their decision making process, ignoring the wealth contained in the important volume of data available to them. Big data processes, infrastructure and attributes, combined with the relatively low cost of data processing thus pave the way for new business models that are available to organizations, especially SMEs with limited resources which could take advantage of better business opportunities.

5-Challenges and future work

The challenges faced by SME concerning the integration of knowledge management activities and big data occurred at two levels, namely on data and organizational basis.

5-1 Data challenges

External data comes from different sources, including social networks, connected objects, research publications, and institutional databases. This impressive production of available data is as much a threat as it is an opportunity.

A great challenge for SMEs is being able to maintain their data sources up to date, and to have access to external data which is behind a pay-wall. Social media companies have understood the value of data and, thus making it difficult for smaller companies to access social media information. Patents are also usually not easily accessible. A way for companies to access this data is to find a vendor from which to license an access to only the information it requires.

A second challenge related to data is the processing of the information. Once the company has surmounted the access challenge, it must then be able to define the right process to be performed on the data. Since resources are scarce in SMEs, the organization has two options to do this processing, assign an internal resource, or resort to a provider. Both options have advantages and disadvantages. Having an internal resource means that the organization can experiment with the questions it wants answers to, but it is a constant expense. Having a provider will drive the managers to figure out the questions before commissioning the development, but the company can profit from professionals who might be able to gear them in new directions.

A third challenge in terms of the data is the insight. As mentioned before, the data is useful when it is being interpreted by someone who has sufficient knowledge of the domain to be able to understand and read the weak signals that can indicate a new trend.

5-2 Organizational challenges

It is necessary that the different functions are effective and efficient and contribute to organizational performance. The fact is that operations are so predominant, that there is a tendency to put limited resources to other functions, including client management, promotion, market segmentation, etc. But the need to create value leads the SME to use the knowledge from all functions.

To be able to profit from it, processing these data requires systematic collection, and a consistent information infrastructure. The purpose is to give a dynamic character to the data and to highlight the evolutionary aspect.

5-3 Future work

To better evaluate the efforts implemented in SMEs to combine big data with knowledge management, the first step is to identify the kind of project the organization want to put in place in a short term period and long term. More than that, SME must be able to establish the link between these projects and the performance of the firm. The purpose is to understand practically how the integration of big data and knowledge management activities contribute to the organizational performance. To do so, we plan to conduct a survey with SME.

The results of the survey will provide an insight to the measures taken and the considerations regarding internal and external data, as well as the strategies to manage the knowledge base (inhouse versus outsource of information systems).

5-Conclusion

SMEs interested in gaining insights and knowledge from internal and external information can turn to new technologies such as big data. The use of this technology will enable the organization to improve their competitiveness by figuring insights in the weak signals of future trends. The knowledge attained from these efforts can lead to the identification of new products, services and complementary resources.

Nonetheless, many aspects need to be taken into account in the implementation of such an initiative. Considerations regarding the data to be analyzed must be taken into account, as the data needs to be reliable. The new tools will allow size and speed of the data to not be a factor; it is the processing that will take center stage, asking the right questions is what will provide the advantage.

Figure 1 .

 1 Figure 1. Integration of KM and Big Data