pub:research:context_aware
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====== GEIST research on Context-Aware systems ====== | ====== GEIST research on Context-Aware systems ====== | ||
- | FIXME | + | The concepts of context and context awareness have been studied for more than 20 years in the field of artificial intelligence, |
+ | |||
+ | Moreover these IT and economical changes are reflecting themselves also onto business applications. | ||
+ | Applications are simplifying, | ||
+ | Hence we are faced with a series of new challenges in the context of developing future business apps. | ||
+ | |||
+ | They need to: | ||
+ | * be highly adaptive; | ||
+ | * provide UIs that are user specific; | ||
+ | * provide means for users to modify them by themselves according to their needs, goals, context, while still keeping the underlying infrastructure in place; | ||
+ | * be interactive; | ||
+ | * be distributed, | ||
+ | * support both desktop and mobile environments, | ||
+ | * developed with business users, and vendors and for customers, while hiding as much as possible all technical requirements. | ||
+ | |||
+ | Considering how wide the research area is, providing a holistic, yet analytic perspective on these concepts remains a challenge. | ||
+ | We employ a new research methodology that aims to address and visualize the topic of context and context awareness from a holistic point of view, by means of text mining and text clustering. | ||
+ | |||
+ | ===== Research methodology ===== | ||
+ | There is a huge amount of work that tackles the problem of context and context awareness in different fields and from different aspects. However, there is no unified view on the matter, nor – to the best of our knowledge – there is any approach that provides a holistic view on the subject. | ||
+ | Therefore we propose a research methodology which takes advantage of the existing techniques for text clustering and text mining to get a broader view on the research that has been done on the side of context and context awareness. | ||
+ | |||
+ | The motivation to use text mining and clustering techniques is very simple. Too many papers that need to be organized, make the task almost impossible to fulfill. Moreover such an approach will provide an automatic way to extract related terms, topics and directions of research. | ||
+ | |||
+ | We present our methodology in a form of a simple workflow, modeled as a business process model, designed using the BPMN ((OMG, " | ||
+ | |{{ : | ||
+ | |Figure 1: Research Methodology - Business Process Model (BPMN notation)| | ||
+ | |||
+ | We have compiled a bibliography file which so far contains 94 carefully selected bibliographic entries that spans over a period of more than 20 years, starting 1991-2013. The quality of the papers is also an important factor. There are two ways to weight and asses the quality of the papers. One way is objective as it is given by the number of citation a paper has. We have extracted the number of citation, where this | ||
+ | number existed, for a paper from digital libraries websites: [[http:// | ||
+ | |||
+ | The steps for compiling this bibliographic collections are depicted in Figure 1. We start by searching via Google for context related keywords i.e. // | ||
+ | |||
+ | The next step in the process (See Figure 1) is to add bibliographic entries. We used for the clustering algorithms the abstract of each paper, if there was one. In consequence a bibliographic entry, if there is one, needs to have an abstract. Some of the papers also contained keywords. We have also used when available the keywords associated. These were combined with the abstract. | ||
+ | |||
+ | We used [[http:// | ||
+ | |||
+ | Although Carrot2 provides several search algorithms we used Lingo and K-Means algorithms as they provided the best results. Unfortunately the free version of Carrot2 does not provide options to addresses issues such as synonyms in order to improve the results. Arthur and Vassilvitskii state in ((D. Arthur and S. Vassilvitskii, | ||
+ | Information Systems, 2004, pp. 359–368.)) as described by the authors is able to capture thematic threads in a search result, that is discover groups of related documents and describe the subject of these groups in a way meaningful to a human. | ||
+ | |||
+ | |{{: | ||
+ | |Figure 2: K-Means - Foam Visual Representation| | ||
+ | |||
+ | |{{: | ||
+ | |Figure 3: Lingo - Foam Visual Representation| | ||
+ | |||
+ | Figures 2 and 3 depict the results of running the K-Means and respectively Lingo algorithms over our bibliographic collection. The results are visualized in a Foam representation. Results are similar but not the same. We can easily visualize directions of research and words related with the context concept. Having similar results it helps to verify the output of the clustering algorithms. Having differences helps to identify what each algorithm has missed with respect to the other. | ||
+ | |||
+ | The authors of " | ||
+ | |||
+ | In addition based on the information depicted in Figures 2 and 3, context has been used to address many of the future business apps challenges we have enumerated in [[: | ||
+ | |||
+ | ==== Files ==== | ||
+ | |||
+ | * Context bibliography file (bibtex inside zip) - {{: | ||
+ | * JabRef2Carrot2 Export Filter - {{: | ||
+ | |||
+ | ==== How to ==== | ||
+ | === JabRef === | ||
+ | - Install custom export format | ||
+ | - Extract carrot2jabrefexportfilter.zip archive | ||
+ | - In JabRef menu: Options-> | ||
+ | - Use for name: Carrot2 | ||
+ | - Browse and use for "Main layout file": carrot2xml.layout | ||
+ | - For "File extension" | ||
+ | - Open contextBiblio.bib file | ||
+ | - File-> | ||
+ | |||
+ | === Carrot2 Workbench === | ||
+ | * In the " | ||
+ | * Choose the clustering algorithm: Lingo, K-means | ||
+ | * Browse and use the file you exported to the bibliography from JabRef | ||
+ | |||
+ | ===== Findings ===== | ||
+ | |||
+ | There has been done a huge amount of work that addresses the problem of context. And although this work has tackled different aspects and | ||
+ | research directions, i.e. modeling, reasoning, data-bases etc., we argue that all this work, from the focus point of view, follows two major directions: context-aware applications that are system-centric (most part of the work) context-aware applications that are user-centric. These two directions act as an analysis framework for us and our further assertions revolve around these directions. | ||
+ | |||
+ | |{{: | ||
+ | |Figure 4: Mind Map of Context Related Concepts for the User-centric perspective| | ||
+ | |||
+ | Figure 4 depicts a mind map with the context related concepts for the user-centric perspective. We argue that the combination of these concepts together with proper techniques for modeling, reasoning and system specific execution facts can address the challenges we | ||
+ | |||
+ | ===== Tools ===== | ||
+ | * JabRef - [[http:// | ||
+ | * Carrot2 - [[http:// | ||
+ | |||
+ | |||
+ | ===== Papers ===== | ||
+ | * | ||
+ | |||
+ | ===== Team ===== | ||
+ | * Emilian Pascalau | ||
+ | * Grzegorz J. Nalepa | ||
+ | * Szymon Bobek | ||
+ | * Krzysztof Kluza | ||
pub/research/context_aware.1368347215.txt.gz · Last modified: 2013/05/12 08:26 by gjn