Nikos Tsirakis

CODE QUALITY EVALUATION METHODOLOGY USING THE ISO/IEC 9126 STANDARD

This work proposes a methodology for source code quality and static behaviour evaluation of a software system, based on the standard ISO/IEC-9126. It uses elements automatically derived from source code enhanced with expert knowledge in the form of quality characteristic rankings, allowing software engineers to assign weights to source code attributes. It is flexible in terms of the set of metrics and source code attributes employed, even in terms of the ISO/IEC-9126 characteristics to be assessed. We applied the methodology to two case studies, involving five open source and one proprietary system. Results demonstrated that the methodology can capture software quality trends and express expert perceptions concerning system quality in a quantitative and systematic manner.

XEdge: clustering homogeneous and heterogeneous XML documents using edge summaries

In this paper we propose a unified clustering algorithm for both homogeneous and heterogeneous XML documents. Depending on the type of the XML documents, the proposed algorithm modifies its distance metric in order to properly adapt to the special structural characteristics of homogeneous and heterogeneous XML documents. We compare the quality of the formed clusters with those of one of the latest XML clustering algorithms and show that our algorithm outperforms it in the case of both homogeneous and heterogeneous XML documents.

Algorithms for clustering clickstream data

Clustering is a classic problem in the machine learning and pattern recognition area, however a few complications arise when we try to transfer proposed solutions in the data stream model. Recently there have been proposed new algorithms for the basic clustering problem for massive data sets that produce an approximate solution using efficiently the memory, which is the most critical resource for streaming computation. In this paper, based on these solutions, we present a new model for clustering clickstream data which applies three different phases in the data processing, and is validated through a set of experiments.

Sentiment Analysis for Reputation Management: Mining the Greek Web

Harvesting the web and social web data is a meticulous and complex task. Applying the results to a successful business case such as brand monitoring requires high precision and recall for the opinion mining and entity recognition tasks. This work reports on the integrated platform of a state of the art Named-entity Recognition and Classification (NERC) system and opinion mining methods for a Software-as-a-Service (SaaS) approach on a fully automatic service for brand monitoring for the Greek language. The service has been successfully deployed to the biggest search engine in Greece powering the large-scale linguistic and sentiment analysis of about 80.000 resources per hour.

Large scale opinion mining for social, news and blog data

A business intelligence platform with social basis.Real-time data filtering in the source.Summarization of historical content.Statistics computation over sliding windows. Companies that collect and analyze data from social media, news and other data streams are faced with several challenges that concern storage and processing of huge amounts of data. When they want to serve the processed information to their customers and moreover, when they want to cover different information needs for each customer, they need solutions that process data in near real time in order to gain insights on the data in motion. The volume and volatility of opinionated data that is published in social media, in combination with the variety of data sources has created a demanding ecosystem for stream processing. Although, there are several solutions that can handle information of static nature and small volume quite efficiently, they usually do not scale up properly because of their high complexity. Moreover, such solutions have been designed to run once or to run in a fixed dataset and they are not sufficient for processing huge volumes of streamed data. To address this problem, a platform for real-time opinion mining is proposed. Based on prior research and real application services that have been developed, a new platform called PaloPro is presented to cover the needs for brand monitoring.

Utilizing XML Clustering for Efficient XML Data Management on P2P Networks

Peer-to-Peer (P2P) data integration combines the P2P infrastructure with traditional scheme-based data integration techniques. Some of the primary problems in this research area are the techniques to be used for querying, indexing and distributing documents among peers in a network especially when document files are in XML format. In order to handle this problem we describe an XML P2P system that efficiently distributes a set of clustered XML documents in a P2P network in order to speed-up user queries. The novelty of the proposed system lies in the efficient distribution of the XML documents and the construction of an appropriate virtual index on top of the network peers.

PaloPro: a platform for knowledge extraction from big social data and the news

PaloPro is a platform that aggregates textual content from social media and news sites in different languages, analyses them using a series of text mining algorithms and provides advanced analytics to journalists and social media marketers. The platform capitalises on the abundance of social media sources and the information they provide for persons, products and events. In order to handle huge amounts of multilingual data that are collected continuously, we have adopted language independent techniques at all levels and from an engineering point of view, we have designed a system that takes advantage of parallel distributed computing technologies and cloud infrastructure. Different systems handle data aggregation, data processing and knowledge extraction and others deal with the integration and visualisation of knowledge. In this paper, we focus on two important text mining tasks, named entity recognition from texts and sentiment analysis to extract the sentiment associated with the corresponding identified entities.

Code4Thought Project: Employing the ISO/IEC-9126 Standard for Software Engineering-Product Quality Assessment

The aim of the Code4Thought project was to deliver a tool supported methodology that would facilitate the evaluation of a software products quality according toISO/IEC-9126 software engineering quality standard. It was a joint collaboration between Dynacomp S.A. and the Laboratory for Graphics, Multimedia and GIS of the Department of Computer Engineering and Informatics of the University of Patras. The Code4thought project focused its research on extending the ISO/IEC-9126standard by employing additional metrics and developing new methods for facilitating system evaluators to define their own set of evaluation attributes. Furthermore, to develop innovative and platform-free methods for the extraction of elements and metrics from source code data.Finally, to design and implement new data mining algorithms tailored for the analysis of software engineering data.

k-ATTRACTORS: A PARTITIONAL CLUSTERING ALGORITHM FOR NUMERIC DATA ANALYSIS

Clustering is a data analysis technique, particularly useful when there are many dimensions and little prior information about the data. Partitional clustering algorithms are efficient but suffer from sensitivity to the initial partition and noise. We propose here k-attractors, a partitional clustering algorithm tailored to numeric data analysis. As a preprocessing (initialization) step, it uses maximal frequent item-set discovery and partitioning to define the number of clusters k and the initial cluster “attractors.” During its main phase the algorithm uses a distance measure, which is adapted with high precision to the way initial attractors are determined. We applied k-attractors as well as k-means, EM, and FarthestFirst clustering algorithms to several datasets and compared results. Comparison favored k-attractors in terms of convergence speed and cluster formation quality in most cases, as it outperforms these three algorithms except from cases of datasets with very small cardinality containing only a few frequent item sets. On the downside, its initialization phase adds an overhead that can be deemed acceptable only when it contributes significantly to the algorithms accuracy.

On the exploitation of relationships in online communities of practice

Numerous tools aiming at facilitating or enhancing collaboration among members of diverse communities have been already deployed and tested over the web. Taking into account the particularities of online communities of practice, this paper introduces a framework for mining knowledge that is hidden in such settings. Our motivation stems from the criticism that contemporary tools receive regarding lack of active participation and limited engagement in their use, partially due to their inability of identifying and meaningfully exploiting important relationships among community members and collaboration-related assets. Particular attention is given to the identification of requirements imposed by contemporary communities and learning contexts. Our overall approach elaborates and integrates issues from the disciplines of data mining and social networking.