Social networking has become one of the most important trends on the Web, leading to the development of several social applications such as blogs. Blogs are locations on the Web where individuals are provided with the ability to express their opinion, experience, and knowledge about a product, an event, or any other subject. The tremendous popularity of these services has rendered the problem of identifying the most influential bloggers significant, since its solution can lead to numerous major benefits for commerce, advertising, and searching. The current works on this topic either ignore temporal aspects or they fail to gracefully incorporate recency, productivity, and influence at the same time. This paper investigates the issue of identifying bloggers who are both productive and influential by introducing the blogger's productivity index and blogger's influence index. The proposed metrics are evaluated against the state-of-the-art influential blogger identification methods by employing data collected from a real-world community blog site. The obtained results confirm that the new methods are able to identify significant patterns in the bloggers' behavior.
The solution of large-scale sparse linear systems arises in numerous scientific and engineering problems. Typical examples involve study of many real world multi-physics problems and the analysis of electric power systems. The latter involve key functions such as contingency, power flow and state estimation whose analysis amounts at solving linear systems with thousands or millions of equations. As a result, efficient and accurate solution of such systems is of paramount importance. The methods for solving sparse systems are distinguished in two categories, direct and iterative. Direct methods are robust but require large amounts of memory, as the size of the problem grows. On the other hand, iterative methods provide better performance but may exhibit numerical problems. In addition, continuous advances in computer hardware and computational infrastructures imposes new challenges and opportunities. GPUs, multi-core CPUs, late memory and storage technologies (flash and phase change memories) introduce new capabilities to optimizing sparse solvers. This work presents a comprehensive study of the performance of some, state of the art, sparse direct and iterative solvers on modern computer infrastructure and aims to identify the limits of each method on different computing platforms. We evaluated two direct solvers in different hardware configurations, examining their strengths and weaknesses both in main memory (in-core) and secondary memory (out-of-core) execution in a series of representative matrices from multi-physics and electric grid problems. Also, we provide a comparison with an iterative method, utilizing a general purpose preconditioner, implemented both on a GPU and a multi-core processor. Based on the evaluation results, we observe that direct solvers can be as efficient as their iterative counterparts if proper memory optimizations are applied. In addition, we demonstrate that GPUs can be utilized as efficient computational platforms for tackling the analysis of electric power systems.
In this paper we present a platform for delivering multimedia presentations on cultural heritage. The platform aims to enhance cultural knowledge discovery by increasing access to museums' digital content. The platform generates rich media presentations considering the personal profile of the audience as well as its interests. The presentations may include text, images, video and sound and can be delivered via network. They can be attended either inside the museum or even outside of it e.g. in schools during a preparation class prior to a museum visit. The platform supports creation and editing of slides and presentations, updating existing presentations and projecting them, considering different roles and access levels for archeologists, tourist guides, educators and individuals.
Nowadays, huge amounts of text are being generated on the Web by a vast number of applications. Examples of such applications include instant messengers, social networks, e-mail clients, news portals, blog communities, commercial platforms, and so forth. The requirement for effectively identifying documents of similar content in these services rendered text clustering one of the most emerging problems of the machine learning discipline. Nevertheless, the high dimensionality and the natural sparseness of text introduce significant challenges that threat the feasibility of even the most successful algorithms. Consequently, the role of dimensionality reduction techniques becomes crucial for this particular problem. Motivated by these challenges, in this article we investigate the impact of dimensionality reduction on the performance of text clustering algorithms. More specifically, we experimentally analyze its effects in the effectiveness and running times of eight clustering algorithms by employing six high-dimensional text datasets. The results indicate that, in most cases, dimensionality reduction may significantly improve the algorithm execution times, by sacrificing only small amounts of clustering quality.
Mobile computing emerged as a new application area due to recent advances in communication and positioning technology. As David Lomet (2002) notices, a substantial part of the conducted work refers to keeping track of the position of moving objects (automobiles, people, etc.) at any point in time. This information is very critical for decision making, and, since objects' locations may change with relatively high frequency, this calls for providing fast access to object location information, thus rendering the indexing of moving objects a very interesting as well as crucial part of the area. In this chapter we present an overview on advances made in databases during the last few years in the area of mobile object indexing, and discuss issues that remain open or, probably, are interesting for related applications.Request access from your librarian to read this chapter's full text.
The past few years have shown a significant increase in the volume and diversity of data stored in database management systems. Among these are spatiotemporal data, one of the faster developing categories of data. This phenomenon can be attributed to the flurry of application development concerning continuously evolving spatial objects in several areas: mobile communication systems, military equipment in battlefields, air traffic, truck fleets, and others. In standard database applications, data remain unchanged unless an update is explicitly stated. Applying this mode of operation to constantly moving objects would require frequent updates to be performed; otherwise, the database would be inaccurate and unreliable. In order to capture continuous movement and to avoid unnecessary updates, object positions are stored as time-dependent functions, requiring updates only when a function parameter changes. The moving objects are considered responsible for updating the database about alterations in their movement. In the following article is a short review on basic indexing schemes for accommodating moving objects in database systems so that complex queries about their location in the past, present, and future can be served.
