Ali Amer Alwan

Processing skyline queries in incomplete distributed databases

Due to its great benefits over many database applications, skyline queries have received formidable concern in the last decades. Skyline queries attempt to assist users by identifying the set of data items which represents the best results that meet the conditions of a given query. Most of the existing skyline techniques concentrate on identifying skylines over a single relation. However, in distributed databases, the process of skyline queries required accessing multiple relations which might be located at different sites. Consequently, data items from these multiple relations need to be joined and thus transferring these data items from one site to another is unavoidable. Moreover, the previous techniques also assume that the values of dimensions for every data item are presented (complete) which is not always true as some values may be missing. In this paper, we proposed an approach for processing skyline queries in incomplete distributed databases. The approach derives skylines from multiple relations where dominated data items are removed before joining the relations to reduce the processing time and the network cost. The experimental results illustrate that our proposed approach outperforms the previous approaches in terms of processing time and network cost.

A Framework for Evaluating Skyline Queries over Incomplete Data

Research interest in skyline queries has been significantly increased over the years, as skyline queries can be utilized in many contemporary applications, such as multi-criteria decision-making system, decision support system, recommendation system, data mining, and personalized systems. Skyline queries return data item that is not dominated by any other data items in all dimensions (attributes). Most of the existing skyline approaches assumed that database is complete and values are present during the skyline process. However, such assumption is not always to be true, particularly in a real world database where values of data item might not be available (missing) in one or more dimensions. Thus, the incompleteness of the data impacts negatively on skyline process due to losing the transitivity property which leads into the issue of cyclic dominance. Therefore, applying skyline technique directly on an incomplete database is prohibitive and might result into exhaustive pairwise comparison. This paper presents an approach that efficiently evaluates skyline queries in incomplete database. The approach aims at reducing the number of pairwise comparisons and shortens the searching space in identifying the skylines. Several experiments have been conducted to demonstrate that our approach outperforms the previous approach through producing a lower number of pairwise comparisons. Furthermore, the result also illustrates that our approach is scalable and efficient.

A framework for evaluating skyline query over uncertain autonomous databases

The perception of skyline query is to find a set of objects that is much preferred in all dimensions. While this theory is easily applicable on certain and complete database, however, when it comes to data integration of databases where each has different representation of data in a same dimension, it would be difficult to determine the dominance relation between the underlying data. In this paper, we propose a framework, SkyQUD, to efficiently compute the skyline probability of datasets in uncertain dimensions. We explore the effects of having datasets with uncertain dimensions in relation to the dominance relation theory and propose a framework that is able to support skyline queries on this type of datasets.

A Framework for Identifying Skylines over Incomplete Data

Skyline queries provide a flexible query operator that returns data items (skylines) which are not being dominated by other data items in all dimensions (attributes) of the database. Most of the existing skyline techniques determine the skylines by assuming that the values of dimensions for every data item are available (complete). However, this assumption is not always true particularly for multidimensional database as some values may be missing. The incompleteness of data leads to the loss of the transitivity property of skyline technique and results into failure in test dominance as some data items are incomparable to each other. Furthermore, incompleteness of data influences negatively on the process of finding skylines, leading to high overhead, due to exhaustive pair wise comparisons between the data items. This paper proposed a framework to process skyline queries for incomplete data with the aim of avoiding the issue of cyclic dominance in deriving skylines. The proposed framework for identifying skylines for incomplete data consists of four components, namely: Data Clustering Builder, Group Constructor and Local Skylines Identifier, k-dom Skyline Generator, and Incomplete Skylines Identifier. Including these processes in the proposed framework has optimized the process of identifying skylines in incomplete database by reducing the necessary number of pair wise comparison through eliminating the dominated data items as early as possible before applying the skyline technique.

Processing Skyline Queries in Incomplete Database: Issues, Challenges and Future Trends

In many contemporary database applications such as multi-criteria decision-making and real-time decision-support applications, data mining, e-commerce and recommendation systems, users need query operators to process the data aiming at finding the results that best fit with their preferences. Skyline queries are one of the most predominant query operators that privileges to find the query results that return only those data items whose dimension vector is not dominated by any other data item in the database. Because of their usefulness and ubiquity, skyline queries have been incorporated into different types of databases such as complete, incomplete and uncertain. This paper attempts to survey and analyze the previous works proposed to process skyline queries in the incomplete database. The discussion focuses on examining these approaches highlighting the strengths and the weaknesses of each work. Besides, we also discuss in detail the current challenges in processing skyline queries in the incomplete database and investigate the impact of incomplete data on skyline operation. A summary of the most well-known works has been reported to identify the limitations of these works. Some recommendations and future work directions have been drawn to help researchers investigate the unsolved problems related to skyline queries in a database system.

Disaster Recovery with Minimum Replica Plan for Reliability Checking in Multi-Cloud

Abstract The primary uses for cloud computing are to store data and share resources. The cloud has become a dominant and preferred method to store large amounts of data and enable the sharing of that data among several users. It also enables the use of pay-as-you-go pricing models. Today’s cloud computing environment has required data centers to increase the amount of available storage. There are two main concerns with cloud storage: data reliability and cost of storage. This paper discusses the issue of data recovery in case of a disaster in a Multi-Cloud environment. We propose a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensure high reliability for data before the disaster. The approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) which is a cost-effective mechanism to reduce the number of replications in the cloud to be 1 or 2-replicas only without compromising the data reliability. The name PDRPMR originates from its preventive action checking of the availability of replicas and monitoring of denial of service attacks to maintain data reliability. Several experiments have been carried out to demonstrate that PDRPMR reduces the amount of storage space used by one third to two-thirds compared to typical 3-replicas replication strategies, which in turn reduces the cost of storage.

A Model for Processing Skyline Queries in Crowd-sourced Databases

Zarina Mohd Noh*, Abdul Rahman Ramli, Marsyita Hanafi, M Iqbal Saripan, Ridza Azri Ramlee 1,2,3,4 Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia 1,5 Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 75450 Durian Tunggal, Melaka, Malaysia

A Survey of Schema Matching Research using Database Schemas and Instances

Schema matching is considered as one of the essential phases of data integration in database systems. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. Many approaches based on instances have been proposed aiming at improving the accuracy of the matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. We survey and analyze the schema matching techniques applied in the literature by highlighting the strengths and the weaknesses of each technique. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in contemporary databases.

Computing Range Skyline Query on Uncertain Dimension

A user sometimes prefers to not be restricted when querying for information. Querying information within a range of search often provides a different perspective to user as opposed to a rigid search. To compute skyline within a given range would be easy on traditional dataset. The challenge is when the dataset being queried consists of both atomic values as well as continuous range of values. For a set of objects with uncertain dimension, a skyline with a range query ( [q_{j} :q_{j} ] ) on that uncertain dimension returns objects which are not dominated by any other objects in the range query. A method is proposed to determine objects and answer skyline query that satisfy the range query. The correctness of the method is proven through comparisons between two naive methods that strictly reject and loosely accept objects that intersect with the range query.