Vassiliki A. Koutsonikola

Co-Clustering Tags and Social Data Sources

Under social tagging systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Poor retrieval in the aforementioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier clustering techniques have shown limited improvements, since they were based mostly on tag co-occurrences. In this paper, a co-clustering approach is employed, that exploits joint groups of related tags and social data sources, in which both social and semantic aspects of tags are considered simultaneously. Experimental results demonstrate the efficiency and the beneficial outcome of the proposed approach in correlating relevant tags and resources.

LDAP: framework, practices, and trends

Directory services facilitate access to information organized under a variety of frameworks and applications. The lightweight directory access protocol is a promising technology that provides access to directory information using a data structure similar to that of the X.500 protocol. IBM Tivoli, Novell, Sun, Oracle, Microsoft, and many other vendors feature LDAP-based implementations. The technologys increasing popularity is due both to its flexibility and its compatibility with existing applications.

Emotional aware clustering on micro-blogging sources

Microblogging services have nowadays become a very popular communication tool among Internet users. Since millions of users share opinions on different aspects of life everyday, microblogging websites are considered as a credible source for exploring both factual and subjective information. This fact has inspired research in the area of automatic sentiment analysis. In this paper we propose an emotional aware clustering approach which performs sentiment analysis of users tweets on the basis of an emotional dictionary and groups tweets according to the degree they express a specific set of emotions. Experimental evaluations on datasets derived from Twitter prove the efficiency of the proposed approach.

A divergence-oriented approach for web users clustering

Clustering web users based on their access patterns is a quite significant task in Web Usage Mining. Further to clustering it is important to evaluate the resulted clusters in order to choose the best clustering for a particular framework. This paper examines the usage of Kullback-Leibler divergence, an information theoretic distance, in conjuction with the k-means clustering algorithm. It compares KL-divergence with other well known distance measures (Euclidean, Standardized Euclidean and Manhattan) and evaluates clustering results using both objective function’s value and Davies-Bouldin index. Since it is imperative to assess whether the results of a clustering process are susceptible to noise, especially in noisy environments such as Web environment, our approach takes the impact of noise into account. The clusters obtained with KL approach seem to be superior to those obtained with the other distance measures in case our data have been corrupted by noise.

Clustering of Social Tagging System Users: A Topic and Time Based Approach

Under Social Tagging Systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Mining tag information reveals the topic-domain of users interests and significantly contributes in a profile construction process. In this paper we propose a clustering framework which groups users according to their preferred topics and the time locality of their tagging activity. Experimental results demonstrate the efficiency of the proposed approach which results in more enriched time-aware users profiles.

Correlating Time-Related Data Sources with Co-clustering

A huge amount of data is circulated and collected every day on a regular time basis. Given a pair of such datasets, it might be possible to reveal hidden dependencies between them since the presence of the one dataset elements may influence the elements of the other dataset and vice versa. Furthermore, the impact of these relations may last during a period instead of the time point of their co-occurrence. Mining such relations under those assumptions is a challenging problem. In this paper, we study two time-related datasets whose elements are bilaterally affected over time. We employ a co-clustering approach to identify groups of similar elements on the basis of two distinct criteria: the direction and duration of their impact. The proposed approach is evaluated using time-related news and stocks market real datasets.

In & out zooming on time-aware user/tag clusters

The common ground behind most approaches that analyze social tagging systems is addressing the information challenge that emerges from the massive activity of millions of users who interact and share resources and/or metadata online. However, lack of any time-related data in the analysis process implicitly denies much of the dynamic nature of social tagging activity. In this paper we claim that holding a temporal dimension, allows for tracking macroscopic and microscopic users’ interests, detecting emerging trends and recognizing events. To this end, we propose a time-aware co-clustering approach for acquiring semantic and temporal patterns out of the tagging activity. The resulted clusters contain both users and tags of similar patterns over time, and reveal non-obvious or “hidden” relations among users and topics of their common interest. Zoom in & out views serve as visualization methods on different aspects of the clusters’ structure, in order to evaluate the efficiency of the approach.

Dynamic Code Generation for Cultural Content Management

Digital repositories are popular means for preserving, restoring, and indexing archaeological and cultural content. They provide the base for development of a fauna of related applications including virtual tours and data management. Common difficulties such as the ever changing software specifications from domain experts make this task challenging as the alterations of the database schema lead to massive code rewrites. Within this context we propose and implement in practice a model for automated code generation building essentially a content management application by traversing a custom tree-based ERschema.

A Clustering-Driven LDAP Framework

LDAP directories have proliferated as the appropriate storage framework for various and heterogeneous data sources, operating under a wide range of applications and services. Due to the increased amount and heterogeneity of the LDAP data, there is a requirement for appropriate data organization schemes. The LPAIR & LMERGE (LP-LM) algorithm, presented in this article, is a hierarchical agglomerative structure-based clustering algorithm which can be used for the LDAP directory information tree definition. A thorough study of the algorithm’s performance is provided, which designates its efficiency. Moreover, the Relative Link as an alternative merging criterion is proposed, since as indicated by the experimentation, it can result in more balanced clusters. Finally, the LP and LM Query Engine is presented, which considering the clustering-based LDAP data organization, results in the enhancement of the LDAP server’s performance.

Automatic extraction of structure, content and usage data statistics of web sites

In this paper we present a web mining tool which automatically extracts the structure, content and usage data statistics of web sites. This work inspired by the fact that web mining consists of three axes: web structure mining, web content mining and web usage mining. Each one of those axes is using the structure, content and usage data respectively. The scope is to use the developed multi-thread web crawler as a tool to automatically extract from web pages data that are associated with each one of those three axes in order afterwards to compute several useful descriptive statistics and apply advanced mathematical and statistical methods. A description of our system is provided as well as some experimentation results.