Fekade Getahun

Relating RSS News/Items

Merging related RSS news (coming from one or different sources) is beneficial for end-users with different backgrounds (journalists, economists, etc.), particularly those accessing similar information. In this paper, we provide a practical approach to both: measure the relatedness, and identify relationships between RSS elements. Our approach is based on the concepts of semantic neighborhood and vector space model, and considers the content and structure of RSS news items.

The use of semantic-based predicates implication to improve horizontal multimedia database fragmentation

Database fragmentation allows reducing irrelevant data accesses by grouping data frequently accessed together in dedicated segments. In this paper, we address multimedia database fragmentation to take into account the rich characteristics of multimedia objects. We particularly discuss multimedia primary horizontal fragmentation and focus on semantic-based textual predicates implication required as a pre-process in current fragmentation algorithms in order to partition multimedia data efficiently. Identifying semantic implication between similar queries (if a user searches for the images containing a car, he would probably mean auto, vehicle, van or sport-car as well) will improve the fragmentation process. Making use of the neighborhood concept in knowledge bases to identify semantic implications constitutes the core of our proposal. A prototype has been implemented to evaluate the performance of our approach.

Releasing the Power of Variability: Towards Constraint Driven Quality Assurance

We identify that Over Design(OD) and Under Design(UD) as two basic forms of negative variability construction which can lead to both functional incompleteness and unexpected quality of both design time and run time. To make our approach practical to a whole software production processes, we propose to evaluate and measure OD and UD in terms of values under economical goals which are concretized with the functionality implementation and the quality management of a quality lifecycle. We work towards relieving OD and UD in terms of variability construction within a variability space.

Native XML Document Fragmentation Model

As XML document is distributed across the web, it can be considered like a distributed repository of XML documents and is subjected to distribution design. However, there is no adequate works on XML document distribution design. To address the shortcomings in XML document fragmentation design, in this work, we have focused on the vertical fragmentation design of the XML documents. Two fragmentation models have been proposed: query based fragmentation and structure-and size-based fragmentation. For the query based fragmentation model, vertical fragmentation techniques are proposed using the bond energy algorithm and graphical based algorithm. We have implemented both algorithms and evaluated their performance. The performance of our fragmentation algorithms are compared with centralized and fully replicated XML documents, where better results are obtained. The Structure- and size-based Fragmentation model and its implementation algorithms are also evaluated and encouraging results are achieved.

Cloud readiness assessment framework and recommendation system

Cloud computing is one of the fastest growing technologies. For developing countries like Ethiopia which has a growing ICT, cloud computing is an attractive choice to adopt. However, the adoption of such a technology should be planned ahead of time taking into consideration the various factors that make adoption successful. The main objective of this research is to propose a cloud readiness assessment framework and an expert system that assesses cloud readiness and recommend which cloud deployment and service model to adopt. The research is grounded by well-studied technological innovation adoption theories: Technology Organization Environment framework (TOE), Diffusion of Innovation (DOI) and Technology Acceptance Model (TAM). Based on these theoretical foundations, a new cloud readiness framework is proposed. A survey is designed based on the framework; using this survey an initial dataset is generated and expanded using synthetic data generator. The expert system relies on predictive modeling for assessing cloud readiness. So, using Weka machine learning platform, J48 decision tree algorithm is experimented using various settings, to train and obtain acceptable model accuracy. Training is performed on the original dataset and synthetically generated dataset. The best obtained model accuracy is 75% with the original dataset.

RSS query algebra: Towards a better news management

Existing XML query algebras are not fully appropriate to retrieve RSS news items mainly due to three reasons: (1) RSS document is text rich and its content is dependent on the wording and verification of the author, thus semantic-aware operators are needed; (2) news items are dynamic and consequently time oriented retrieval is needed; and (3) a news item may evolve through time, or overlap with other news items and hence identifying relationships between items is needed. In this paper, we aim to solve these issues by providing a dedicated RSS algebra based on semantic-aware operators which are capable of considering RSS characteristics. The provided operators are application domain specific and can be tuned according to the user preferences. We also provide a set of query rewriting and equivalence rules that would be used during query simplification and optimization. In addition and in order to validate our proposal, we develop a prototype called EasyRSSManager that allows a user to formulate RSS query using our operators.

A General Multimedia Representation Space Model toward Event-Based Collective Knowledge Management

Emergent technologies such as smart phones and wireless Internet have transformed the Web from a static data publishing platform into a collaborative information sharing environment. Yet, attaining the next stage in Web engineering, i.e., the so-called Intelligent Web: allowing meaningful human-machine and machine-machine collaboration, requires another breakthrough: allowing the sharing and organization of collective knowledge (CK), where CK underlines the combination of all known data, information, and meta-data concerning a given concept or event. In this context, various methods have been put forward to perform automatic event extraction and description. Yet, most of them do not capture the semantic meaning embedded in Web-based multimedia data, which are usually highly heterogeneous and unstructured. To address this problem, we introduce in this study a generic Multimedia Representation Space Model called MRSM, designed for multimedia data and multimedia-based event representation, in order to allow event detection and identification based on multimedia CK. We formally define MRSM, its dimensions, their coordinates, and the associated distance (similarity) metrics and properties. We then provide the building blocks for an Eventbased Collective Knowledge (CK) Management Framework, built upon MRSM, and geared toward effective CK management. The proposed approach provides a means of extracting, representing, and linking events from heterogeneous multimedia data without any prior knowledge about event-related clues. Preliminary tests confirm the quality and potential of our approach.

Multi-query optimization for semantic news feed query

Recently, a window-based and semantic-aware feeds querying operators to retrieve set of news items satisfying a given condition has been proposed. The use of semantic information improves the relevance of query result at the cost of degrading the efficiency of the system. To benefit from query execution on semantic information while keeping efficiency of the system, we have proposed a multi-query optimization strategy. The proposed MQO strategy accepts semantic news feed queries and examines the relationship that exists between windows defined on queries. Then, a MQE chain is generated based on the window relationship examined for the efficient manipulation of queries. Whenever a new query arrives, it is added to its appropriate chain. To validate the proposed approach, we have developed a prototype and the experimental results show that the use of our approach improves significantly the performance of the system.

Towards Semantic-based RSS Merging

Merging information can be of key importance in several XML-based applications. For instance, merging the RSS news from different sources and providers can be beneficial for end-users (journalists, economists, etc.) in various scenarios. In this work, we address this issue and mainly explore the relatedness relationships between RSS entities/elements. To validate our approach, we also provide a set of experimental tests showing satisfactory results.

Multi-Query Optimization on RSS Feeds

RSS feeds are text-content rich, semantically heterogeneous, and contain dynamic XML elements streamed in asynchronous and pull strategies. Hence, for efficient retrieval of RSS feeds, semantic-aware querying operators have been proposed in the literature (Getahun and Chbeir in Inf Sci 237(237):313–342, 2013). However, it is commonly admitted that the use of semantic information would improve, on one hand, the relevance of query result but, on the other hand, at the cost of degrading the efficiency and the performance of the system. To benefit from query execution on semantic information while keeping the efficiency of the system, we propose here a multi-query optimization approach for semantic RSS feed queries. Our approach processes queries by examining the semantic relationship between them and their corresponding windows. It generates a multi-query chain for queries using their window relations for faster execution at runtime. In addition, we propose an operator called quickDrop for semantic load shedding to gracefully decrease irrelevant data load. To validate the proposed approach, we developed a prototype and conducted a set of experiments. The obtained results show that the use of our approach significantly improves the performance of the system.