https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Language in relation to a unified theory of the structure of human

This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The "labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling. Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster, much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text, pure statistical methods have difficulty in identifying them as good descriptors. Furthermore, our experiments show that for more than 85% of the clusters in our test collection, the manual label (or an inflection, or a synonym of it) appears in the top five labels recommended by our system.

/pdf/enhancing-cluster-labeling-using-wikipedia-512mvy5ppw.pdf

Enhancing cluster labeling using wikipedia

A new paradigm, known as Enterprise Mashups, has been gain momentum during the last years. By empowering actual business end-users to create and adapt individual enterprise applications, Enterprise Mashups implicate a shift concerning a collaborative software development and consumption process. Upcoming Mashup tools prove the growing relevance of this paradigm in the industry, both in the consumer and enterprise-oriented market. However, a market overview of the different tools is still missing. In this paper, we introduce a classification of Mashup tools and evaluate selected tools of the different clusters according to the perspectives general information, functionality and usability. Finally, we classify more than 30 tools in the designed classification model and present the observed market trends in context of Enterprise Mashups.

/pdf/market-overview-of-enterprise-mashup-tools-3ayw0woy4o.pdf

Market Overview of Enterprise Mashup Tools

The WWW is currently experiencing a revolutionary growth due to its increasing participative community software applications. This paper highlights an emerging application development paradigm on the WWW, called mashup. As blogs have enabled anyone to become a publisher, mashups stimulate web development by allowing anyone to combine existing data to develop web applications. Current applications of mashups include tracking of events such as crime, hurricanes, earthquakes, meta-search integration of data and media feeds, interactive games, and as an organizer for web resources. The implications of this emerging web integration and structuring paradigm remains yet to be explored fully. This paper describes mashups from a number of angles, highlighting current developments while providing sufficient illustrations to indicate its potential implications. It also highlights the role of mashups in complementing and enhancing digital journals by providing insights into the quality academic content, extent of coverage, and the enabling of expanded services. We present pioneering initiatives for the Journal of Universal Computer Science in our efforts to harness the collective intelligence of a collaborative scholarly network.

/pdf/mashups-emerging-application-development-paradigm-for-a-42i48e9fd6.pdf

Mashups: Emerging Application Development Paradigm for a Digital Journal

The web has caused an explosion of documents, requiring the need for an automated text categorization system. This paper explores the notion of semantic feature selection by employing WordNet [Introduction to WordNet: An On-line Lexical Database], a lexical database. The proposed semantic approach employs noun synonyms and word senses for feature selection to select terms that are semantically representative of a category of documents. The categorical sense disambiguation extends the use of WordNet, which has been typically used for text retrieval and word sense disambiguation [A WordNet-based Algorithm for Word Sense Disambiguation]. Our experiments on the Reuters-21578 dataset have shown that automated semantic feature selection is able to perform better than well known statistical feature selection methods, Information Gain and Chi-Square as a feature selection method.

Semantic Feature Selection Using WordNet

We are approaching an era where research materials will be stored more and more as digital resources on the World Wide Web. This of course will enable easier access to online publications. As the number of electronic publications expands, it will, however, become a challenge for individuals to find related or relevant papers. Related papers could be papers written by the same team of authors or by one of the authors, or even papers that deal with the same topic but were written by other authors. This, of course, raises the issue of linking to papers forward in time, or as we call it "links into the future". To be concrete, while reading a paper written in the year 1980, it would be nice to know if the same author has written another related paper in 1990's or if the same author has written a paper earlier, all this without making an explicit search. Based on the ascertained interest of a person reading a particular paper from a digital repository, an auto-suggestion facility could be useful to indicate papers in the same area, category and subject that might potentially be of interest to the reader. One is typically interested in finding related papers by the same author or by one of the authors of a paper. This feature can be implemented in two ways. The first is by creating links from this paper to all the relevant papers and updating it periodically for new papers appearing on the World Wide Web. Another way is by going through the references of all papers appearing on the WWW. Based on the references, one can create mutual links to the papers that are referred to. In this paper, we focus on offering personalised services beyond standard global access. We explore means of identifying the relevance (or relatedness) of papers. A related paper can mean different things to different people as explained above. Ideally, related papers are found and made accessible using links into the future that could be customised to suit the needs of individual users. In this paper, we will focus on a subset of the problem. We explore links into the future in the context of a particular journal which has existed for the past 13 years with over 1500 published papers. We discuss problems that arise in this restricted context while providing details of partial implementations. We plan to pursue our ideas in a more general setting in future implementations.

/pdf/creating-links-into-the-future-3zhrk46owi.pdf

Creating Links into the Future

Everyone realizes how powerful the few big Web search engine companies have become, both in terms of financial resources due to soaring stock quotes and in terms of the still hidden value of the wealth of information available to them. Following the common belief that “information is power” the implications of what the data collection of a de-facto monopolist in the field like Google could be used for should be obvious. However, user studies show that the real implications of what a company like Google can do, is already ...

/pdf/restricting-the-view-and-connecting-the-dots-dangers-of-a-1g41usp4mv.pdf

Restricting the view and connecting the dots: Dangers of a web search engine monopoly

Citations management is an important task in managing digital libraries. Citations provide valuable information e.g., used in evaluating an author's influences or scholarly quality (the impact factor of research journals). But although a reliable and effective autonomous citation management is essential, manual citation management can be extremely costly. Automatic citation mining on the other hand is a non-trivial task mainly due to non-conforming citation styles, spelling errors and the difficulty of reliably extracting text from PDF documents. In this paper we propose a novel rule-based autonomous citation mining technique, to address this important task. We define a set of common heuristics that together allow to improve the state of the art in automatic citation mining. Moreover, by first disambiguating citations based on venues, our technique significantly enhances the correct discovery of citations. Our experiments show that the proposed approach is indeed able to overcome limitations of current leading citation indexes such as ISI Web of Knowl- edge, Citeseer and Google Scholar. Categories and Subject Descriptors H.3.7 (Digital Libraries); System issues: H.3.3 (Information Search and Retrieval); Retrieval models: I.1.2 (Algorithms); Analysis of algorithms: I.2.8 (Problem Solving, Control Methods, and Search);

http://www.dline.info/fpaper/jdim/v8i3/8.pdf

Narayanan Kulathuramaiyer

Papers

Mashups: Emerging Application Development Paradigm for a Digital Journal

Semantic Feature Selection Using WordNet

Creating Links into the Future

Restricting the view and connecting the dots: Dangers of a web search engine monopoly

Rule based Autonomous Citation Mining with TIERL