Showing papers by "Ramez Elmasri published in 2007"

Energy Efficient Spatial Query Processing in Wireless Sensor Networks

Abstract: Because a sensor network depends on limited battery power, energy saving is important to increase the sensor network lifespan. We propose semi-distributed spatial query indexing structure that disseminates a query into the network and retrieves data energy efficiently using a localized tree building algorithm. We also propose a sectioned tree index, which divides the network area into several squares and each square has a local index subtree organized within that square. Local trees are interconnected to form one big tree in the network. Local trees are also built based on any algorithm that is energy consumption aware at each sub-root node in a locally centralized way. We use an existing two dimensional indexing technique for energy efficient query dissemination. We show that our proposed scheme is energy efficient for query and data processing heuristically. Our proposed scheme, sectioned tree, is finally simulated in sparse and dense networks to show the energy saving for query and data processing in the sensor network.

Multi-level Conceptual Modeling for Biomedical Data and Ontologies Integration

Abstract: In biomedical/biological research fields, some semantic data (experiments, evidence, annotation, etc.) can be classified into different levels (from the DNA/RNA level to higher levels such as cells, tissues, organs, and biological systems) based on different degrees of abstraction. Hence, it is important to represent and integrate them across different levels. The purpose of this paper is to address the characteristics of biological data and develop a multi-level conceptual model. Our approach facilitates more accurate modeling of biomedical ontologies, and a better understanding of the data stored in various biological data sources. Furthermore, we propose intra-level and inter-level relationships and give formal definitions illustrated with examples in order to precisely describe relation/behavior among concepts at multiple levels. Our work can be used as the building block for biomedical data or ontologies integration.

OOXsearch: a search engine for answering loosely structured XML queries using OO programming

Abstract: There has been extensive research in XMLkeyword-based and loosely structured querying. Some frameworks work well for certain types of XML data models and fail in others. The reason is that the proposed techniques are based on finding relationships between solely individual nodes while overlooking the context of these nodes. The context of a leaf node is determined by its parent node, because it specifies one of the characteristics of its parent node. Building relationships between individual leaf nodes without consideration of their parents may result in relationships that are semantically disconnected. Since leaf nodes are nothing but characteristics of their parents, we observe that we could treat each parent-children set of nodes as one unified entity.We then find semantic relationships between the different unified entities. Based on those observations, we propose an XML semantic search engine called OOXSearch, which answers loosely structured queries. The recall and precision of the engine were evaluated experimentally and compared with two recent proposed systems [1, 2] and the results showed marked improvement.

Modelling concepts and database implementation techniques for complex biological data

Abstract: Biological data such as protein structure and function, DNA sequences, and metabolic pathways require conceptual modelling characteristics that are not available in the widely used Entity-Relationship (ER) model and its variants, such as the Enhanced-Entity Relationship (EER) model. In particular, three constructs that occur frequently in bioinformatics data are ordered relationships, functional processes, and 3-dimensional (3D) structures. In this paper, we suggest a solution to this problem, requiring only minimal changes to the EER model by introducing specialised formal relationships for ordering, processes and molecular spatial structure. We show how these new concepts can be implemented in relational databases.

Energy Balanced In-Network Aggregation Using Multiple Trees in Wireless Sensor Networks

Abstract: Advances in wireless networks are expected to play an increasing role in systems that are aimed at collecting information. One of the main challenges in wireless sensor networks is that a sensor node has limited battery power. Therefore in order to increase the lifetime of sensor nodes, we need to reduce the amount of energy consumption. For reducing energy consumption in sensor networks, in-network aggregation is one of the proposed methods. However in-network aggregation does not keep the energy balance if some nodes are on the most frequently used paths in a network such as sink node. In order to consider more energy efficiency through load balancing, we propose a new in-network aggregation structure based on multiple trees, called MULT, for further extending the lifetime of in-network aggregation. Unlike existing in-network aggregation structures, which aim to reduce communication cost, the proposed MULT further provides energy balance. MULT has 3phases: first building the clusters, second connecting the clusters and third making multiple trees. MULT is based on creating node clusters using distance between nodes. In addition, a new clustering method, called HYC (HYbrid Cluster) is introduced for MULT structure. We compare the MULT with LEACH and EAD, which are popular in-network aggregation methods. MULT outperforms LEACH and EAD for energy load balance. KeywordsSensor Network, Energy Load Balance, In-Network aggregation

Mediated taxonomy system for bioinformatics data integration

Abstract: In biomedical/biological research fields, scientists usually face the problem of information explosion. Once a query is issued, a collection of data instance IDs such as protein accession numbers are returned. Each protein must be checked for the features of interest. This paper addresses the characterization and differentiating of biological data in the context of mediated domain ontology based on the multilevel abstraction framework. Our mediator system features hybrid ontologies (internal core ontology concept and external classification/annotation concepts) for the interpretation of protein and gene instance data in the context of interaction, pathway and process, which achieve the goal of data integration at the instance level.

XPCache: An Efficient Query Processor for Client-Server Architecture

Abstract: This study addresses the problem of distributed XML queries in client-server architecture. Distributed XML documents can be large and complex. Due to the overhead involved in decomposing the queries, sending the decomposed queries to remote site(s), and executing structural join operations to compose the results, it can be difficult to rapidly execute queries on these distributed XML docs. We propose a query processor called XPCache to tackle those problems. We describe a Global Query Graph, a novel mechanism used by XPCache to classify queries based on their type and frequency. We then describe XPCache's mechanisms that exploit those classifications to improve query response time. Queries classified under the same type are given the same query optimization plan. These plans are cached at the central site. Queries classified as frequently used are answered from the central site cache, which improves query response time. The performance of XPCache is compared with another system and the results showing marked performance improvement.