In the last years, uncertainty management became an important aspect as the presence of uncertain data increased rapidly. Due to the several advanced technologies that have been developed to record large quantity of data continuously, resulting is a data that contain errors or may be partially complete. Instead of dealing with data uncertainty by removing it, we must deal with it as a source of information. To deal with this data, database management system should have special features to handle uncertain data. The aim of this paper is twofold: on one hand, to introduce some main concepts of uncertainty in database by focusing on different data management issues in uncertain databases such as join and query processing, database integration, indexing uncertain data, security and information leakage and representation formalisms. On the other hand, to provide a survey of the current database management systems dealing with uncertain data, presenting their features and comparing them.
Query caching has been utilized efficiently to improve query processing in dismbuted database environments. Most prior caching techniques are based on single-level caching of previous query results. This is basically to avoid accessing the underlying databases each time a user submits the same query. In this paper, we propose a new methodology that allows caching a combination of both plans and results of prior queries in a multilevel caching architecture. The objective is to reduce the response time of distributed query processing and hence increase the system throughput.
-Caching frequently asked queries is an effective way to improve the performance of both centralized and distributed database systems. Intensive works have been done in this area to propose different query caching techniques and to evaluate their performance. However, most of these works were confined to caching previous query results in a single-level caching architecture. Evaluations of these works were based on simulations. In [1], we proposed a new query caching technique for caching both query results and execution plans in a multi-level caching architecture. The centralized version of this technique was evaluated and the results were reported in [2]. In this paper, we present an analytical model to evaluate the performance of the proposed technique in distributed database systems.
Caching frequently asked queries is an effective way to improve the performance of both centralized and distributed databases. Intensive works have been done in this area to propose different query caching tecnniques and to evaluate the performance of these techniques. However, most of these works were confined to caching previous query results in a singlelevel caching architecture. Evaluations of these techniques were based on simulations. In this paper, we briefly discuss our innovative technique for caching both query results and execution plans in a multi-level caching architecture. Then we also present an analytical model to evaluate the performance of the proposed technique and compare it to the traditional query optimizer.
Keyword Search Over Relational Databases (KSORDB) provides an easy way for casual users to access relational databases using a set of keywords. Although much research has been done and several prototypes have been de veloped recently, most of this research implements exact (also called syntactic or keyword) match. So, if there is a vocabulary mismatch, the user cannot get an answer although the database may contain relevant data. In this paper we propose a system that overcomes this issue. The proposed system extends exist ing schema-free KSORDB systems with semantic match fea tures. It exploits domain ontology to progressively return re lated terms that can be used to retrieve more relevant answers to user. Experimental results show that the semantic search method, employed by the proposed system, is more effective than the traditional keyword search method, employed by the existing schema-free KSORDB systems, in terms of the recall rate of the retrieved results.A signiticant amount of plain text and structured data has been stored side by side in relational databases for decades. In order to query this data, users have to know the database schema and then use Structure Query Language (SQL) to issue a precise, unambi guous and well-formed query. To relieve users from doing this, Keyword Search over Relational Database (KSORDB) enables casual users to query this data using a set of keywords called key word query. Existing KSORDB approaches can be categorized into two main categories: Schema-based approaches [1-3] and schema-free approach [4][5][6][7]. In order to process a keyword query, the schema-based approach uses the schema graph to generate Candidate Network (CNs) and then evaluate these networks using SQL queries. While in the second approach, schema-free, the data base is modeled as a data graph where nodes are tuples and edges are foreign -primary key relationships. The graph is then tra versed, at the query time, to answer keyword queries.The main difficulties with the schema-based KSORDB sys tems are in generating optimal SQL queries from a huge number of CNs. Moreover, the generated queries usually contain many join operations which are characterized by complex processing and time consumption features. Schema-free KSORDB systems have three main drawbacks: First, they are not efficient for large size databases as they consume the memory in materializing and processing the data graph. Second, the data graph should be up dated each time the underlying database is updated; therefore, this model is not appropriate for databases that change frequently. Third, traversing a huge data graph to tind minimum Steiner trees is proved to be an NP-hard problem [8]. Furthermore, both KSORDB approaches implement exact keyword matching/ search without exploiting the semantic relationships, such as such as meronym, hyponymy, and antonym, between keywords to find the correlated data.In Information Retrieval (IR) community, Ontology is used to support semantic matching. "An ontology is an explicit specifica tion of a conc...
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