Panayiotis Bozanis

Signature-based structures for objects with set-valued attributes

Aiming at the efficient retrieval of objects with set-valued attributes, we introduce three variations of a new method in order to satisfy subset and superset queries. Our approach is to combine the advantages of two access methods, that of linear Hashing and of tree-shaped methods, on which other similar methods have been previously reported as well. Performance estimation analytical functions for each particular method are presented, followed by a thorough experimental comparison of all investigated structures, where analytical and experimental results deviate 10% on the average. Finally, the results of this performance evaluation are presented and discussed, clearly showing the superiority of the new methods reaching an improvement of up to 85%.

Business Application Acquisition: On-Premise or SaaS-Based Solutions?

The benefits of migrating business software applications to the cloud is a dominant IT topic among consultants, software managers, and executives. The broad interest in cloud computing is motivated by the prospect of quick, painless deployment and maintenance of applications that are now a burden of the enterprise. The authors propose an analytical method for deciding whether the features and cost of a cloud solution are appropriate to the business IT problem and whether the risks are reasonable and manageable.

Identifying Influential Bloggers: Time Does Matter

Blogs have recently become one of the most favored services on the Web. Many users maintain a blog and write posts to express their opinion, experience and knowledge about a product, an event and every subject of general or specific interest. More users visit blogs to read these posts and comment them. This “participatory journalism” of blogs has such an impact upon the masses that Keller and Berry argued that through blogging “one American in tens tells the other nine how to vote, where to eat and what to buy” [9]. Therefore, a significant issue is how to identify such influential bloggers. This problem is very new and the relevant literature lacks sophisticated solutions, but most importantly these solutions have not taken into account temporal aspects for identifying influential bloggers, even though the time is the most critical aspect of the Blogosphere. This article investigates the issue of identifying influential bloggers by proposing two easily computed blogger ranking methods, which incorporate temporal aspects of the blogging activity. Each method is based on a specific metric to score the blogger’s posts. The first metric, termed MEIBI, takes into consideration the number of the blog post’s inlinks and its comments, along with the publication date of the post. The second metric, MEIBIX, is used to score a blog post according to the number and age of the blog post’s inlinks and its comments. These methods are evaluated against the state-of-the-art influential blogger identification method utilizing data collected from a real-world community blog site. The obtained results attest that the new methods are able to better identify significant temporal patterns in the blogging behaviour

A Platform for Delivering Multimedia Presentations on Cultural Heritage

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.

Effective rank aggregation for metasearching

Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the search systems, the effectiveness of a metasearch engine is mainly determined by the quality of the results it returns in response to user queries. Since these services do not maintain their own document index, they exploit multiple search engines using a rank aggregation method in order to classify the collected results. However, the rank aggregation methods which have been proposed until now, utilize a very limited set of parameters regarding these results, such as the total number of the exploited resources and the rankings they receive from each individual resource. In this paper we present QuadRank, a new rank aggregation method, which takes into consideration additional information regarding the query terms, the collected results and the data correlated to each of these results (title, textual snippet, URL, individual ranking and others). We have implemented and tested QuadRank in a real-world metasearch engine, QuadSearch, a system developed as a testbed for algorithms related to the wide problem of metasearching. The name QuadSearch is related to the current number of the exploited engines (four). We have exhaustively tested QuadRank for both effectiveness and efficiency in the real-world search environment of QuadSearch and also, using a task from the recent TREC-2009 conference. The results we present in our experiments reveal that in most cases QuadRank outperformed all component engines, another metasearch engine (Dogpile) and two successful rank aggregation methods, Borda Count and the Outranking Approach.

A network aware privacy model for online requests in trajectory data

In this work, we propose a privacy model for online user requests on trajectory data in location based services by utilizing an underlying network of user movement. The privacy model has been implemented as a framework that (i) reconstructs the user movement from a series of independent location updates, (ii) identifies routes where user privacy is at risk, and (iii) transforms user requests into anonymous equivalents. The framework is implemented on top of a spatial database engine that offers a significant portion of the necessary functionality, while provides for its effective deployment.

New Results on Intersection Query Problems

We present simple algorithms for three problems belonging to the class of intersection query problems. The first algorithm deals with the static rectangle enclosure problem and can easily be extended to d dimensions, the second algorithm copes with the generalized c-oriented polygon intersection searching problem in two dimensions, while the third solves the static 2-dimensional dominance searching problem with respect to a set of obstacles. All algorithms are simple, are based on persistence and improve previous bounds. Also, as a corollary of the first algorithm, we present a result for the static d-dimensional range searching problem.

Application Development: Fly to the Clouds or Stay In-house?

Cloud computing is a recent trend in IT that moves computing and data away from desktop and portable PCs into large data centers, and outsources the “applications” (hardware and software) as services over the Internet. Cloud computing promises to increase the velocity with which applications are deployed, increase innovation, and lower costs, all while increasing business agility. But, is the migration to the Cloud the most profitable option for every business? This article presents a study of the basic parameters for estimating the potential costs deriving from building and deploying applications on cloud and on-premise assets.

LR-tree: a Logarithmic Decomposable Spatial Index Method

Since its introduction in 1984, R-tree has been proven to be one of the most practical and well-behaved data structures for accommodating dynamic massive sets of geometric objects and conducting a very diverse set of queries on such datasets in real-world applications. This success has led to a variety of versions, each one trying to tune the performance parameters of the original proposal. Among them, the most prominent one is R ∗ -tree, which employs a number of carefully designed heuristicsand is widely accepted as achieving the best performance in most cases. However, in the presence of actively changing datasets, R ∗ -tree still does not avoid performance tuning with forced reinsertion, i.e. a process that performs a kind of local rebuilding. The latter fact has motivated the investigation of the adaptation of a known dynamization technique, based on carefully triggered local rebuildings, for converting static or semi-dynamic, main memory data structures to dynamic ones onto R ∗ -trees. In this paper, we present LR-trees, a new efficient scheme for dynamic manipulation of large datasets, which combines the search performance of the bulkloaded R-trees with the updated performance of R ∗ -trees. Experimental results provide evidence on the latter statement and illustrate the superiority of the proposed method.

iMuse Mobile Tour: A personalized multimedia museum guide opens to groups

In the recent years, there is a growing interest in exploiting the advances of mobile and pervasive computing to museum environments. A mobile museum guide, named iMuse Mobile Tour is presented in this paper. The guide utilizes UHF RFID technology to provide context aware information services. It comprises predefined and self-defined tours as well as interactive games to stimulate learning. A group support service is introduced, which enables group visitors to exploit guidance services on their private mobile phones. The service provides access to information retrieved by museums RFID enabled devices.