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Context: A key issue when dealing with the generalization threat of software engineering experiments is to use different subject types. Objective: In this paper, we aim to investigate which subject types are used in experiments and their impact on results. Method: We have performed a systematic mapping study by manually searching experiments published from January 2014 to June 2016 in six leading...
The Levy Walk (or Levy flight) is a concept fromBiomathematics to describe the hunting–behaviour of manypredatory species. It is a very efficient way to find prey in avery short time frame. We now want to use this concept ina clustering–context to – if you so will – "hunt" for clusters. We describe how we convert this concept into an efficient wayto find cluster centres by linking the data...
Semi-supervised learning is the required paradigm when data are partially labeled. It is more adapted for large domain applications when labels are hardly and costly to obtain. In addition, when data are large, feature selection and instance selection are two important dual operations for removing irrelevant information. To address theses challenges together, we propose a unified framework, called...
In order to generate effective results, it is essential for a recommender system to model the information about the user interests (user profiles). A profile usually contains preferences that reflect the recommendation technique, so collaborative systems represent a user with the ratings given to items, while content-based approaches assign a score to semantic/text-based features of the evaluated...
There is an increasing need to quickly understand the contents log data. A wide range of patterns can be computed and provide valuable information: for example existence of repeated sequences of events or periodic behaviors. However patternminingtechniquesoftenproducemanypatternsthathave to be examined one by one, which is time consuming for experts. On the other hand, visualization techniques are...
Understanding customer buying patterns is of great interest to the retail industry. Association rule mining is a common technique for extracting correlations such as people in the South of France buy rosé wine or customers who buy paté also buy salted butter and sour bread. Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular...
Open Source Software (OSS) hosted in Repositories such as GitHub can be valuable as a source of information for requirements engineers, especially in the apprentice phase of a new application. In this context, we propose a strategy to speed up the discovery of valuable information, since manual search may be time consuming in the vast dataset of GitHub projects. Our strategy is based on the identification...
A task at the beginning of the software development process is the creation of a requirements specification. The requirements specification is usually created by a software engineering expert. We try to substitute this expert by a domain expert (the user) and formulate the problem of creating requirements specifications as a search-based software engineering problem. The domain expert provides only...
Integrating data from very large, dynamic, heterogeneous and autonomous data sources is a key requirement to satisfy growing information needs. In order to allow for ad-hoc answering of analytical questions, necessary up-front integration effort must be minimized and data integration systems must be adapted to the expectations and requirements of their users. While existing approaches offer support...
This paper describes a method for extracting the search contexts on the basis of the analysis of search history data such as viewed Web pages, search queries, and bookmarks during collaborative exploration activities. There are many opportunities for collaborative exploration in the form of cooperative work at educational organizations such as universities. It is quite significant for collaborative...
Interactions in many real-world phenomena can be explained by a stronghierarchical structure. Typically, this structure or ranking is not known, instead we only have observed outcomes of the interactions, and the goal is toinfer the hierarchy from these observations. Discovering a hierarchy in the context of directed networks can be formulated asfollows: given a graph, partition vertices into levels...
New distribution channels like music streaming platforms paved way for making more and more diverse music available to users. Thus, music recommender systems got in the focus of research in academia as well as industry. Collaborative filtering-based recommender systems have been proven useful, but there is space left for improvements by adapting this general approach to better fit to the music recommendations...
Measuring "similarity" has been established as afundamental problem and has been widely studied. In thispaper we propose a novel approach for establishing similarityin context of citation network. With the rapidly growing sizeof academic literature, the problem of finding similar researchpapers has become a challenging task. Research papers in acitation network often form communities based...
Finding appropriate adslots to display ads is an important step to achieve high conversion rates in online display advertising. Previous work on ad recommendation and conversion prediction often focuses on matching between adslots, users and ads simultaneously for each impression at micro level. Such methods require rich attributes of users, ads and adslots, which might not always be available, especially...
ToMaR provides a flexible application for integrating existing software into data-flow applications that execute on top of a MapReduce-based environment. The application supports a Linux-inspired pipes-and-filter based syntax, the execution of existing applications using file and stream based IO, and the efficient integration with existing data-flow frameworks like Apache Pig.
This paper proposes an approach of the spatio-temporal data mining in order to predict next learning steps (next ubiquitous learning logs to be learned) in accordance with their situations or context from past learners' experiences in their daily lives accumulated in the ubiquitous learning system called SCROLL (System for Capturing and Reminding of Learning Log). Ubiquitous learning log (ULL) is...
Gamification is an emerging trend in service engineering with a perspective on engagement management. Gamification applications include marketing, enterprise management, education, and health-care. Gamification facilitates user behavior design and social experience design. However, there are many obstacles to successful gamification in an enterprise context. The author discusses the challenges and...
With the advancement in wireless sensor networks (WSN) researchers in social network analysis (SNA) now have access to larger and more complex datasets that describe human interactions in the physical space. Studies in WSN thrive on accuracy and robustness whereas SNA operates on a higher level of data abstraction. Graph mining is a bridge between these two fields. This paper investigates two approaches...
This paper presents some options of including Open Source Intelligence (OSINT) reports in Business to Business (B2B) platforms. Different approaches are investigated with a focus on the business needs for OSINT and an example is given in the form of the current implementation inside the “Cloud.ro” B2B platform.
In the past decade, online social networks have provided invaluable data in understanding how social networks change in time while attracting new users and fostering relationships among members. The concept of social trust was developed to explain why and how much users trust each other to become friends or expose their personal data. Existing work on social trust analyze behavioral features and profile...
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