Papers by Keyword: Query Processing

Abstract: Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this paper, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

Abstract: This paper presents a probabilistic data stream clustering method P-Stream. An effective clustering algorithm called P-Stream for probabilistic data stream is developed in this paper for the first time. For the uncertain tuples in the data stream, the concepts of strong cluster, transitional clusters and weak cluster are proposed in the P-Stream. With these concepts, an effective strategy of choosing candidate cluster is designed, which can find the sound cluster for every continuously arriving data point. In this paper, we systematically defined the dataspace, the uncertain data, and proposed a updated algorithm of queries on uncertain data based on Effective Clustering Algorithm.

Abstract: Bottom-up algorithm, which is one of the two probabilistic Top-k query algorithms, was improved. The core of the bottomup algorithm is the iteration on the three courses of bounding, pruning,and refining towards the objects and instances. The main contribution is to change the iteration on instances of objects one by one into iterating all the instances of objects from the superior to the inferior;and to transform the condition and sequence of pruning in order to make the pruning more effective. Theoretical analysis and experimental results show that the algorithm efficiency could be obviously increased by about 20%.

Abstract: Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

Abstract: In this paper, we propose two bloom filter based algorithms to solve the complicated event query adopting the separate DCS scheme. By using bloom filter, the communication cost involved in the procedure of query processing can be reduced. Experimental results show that our bloom filter based complicated event query processing algorithms achieve significant performance improvement in terms of energy consumption.

Abstract: Due to the proliferation of low cost wireless sensors, there is growing research interest in their applications, for example, in home healthcare and location tracking. However, due to sensors’ energy resource constraint, some possible applications of sensors have been restricted. In particular, in applications concerning deployment of mobile sensors in dynamic environments, high amounts of energy are consumed by sensors to maintain routing tables. Although existing methods have been proposed to query data from sensors without the use of any routing tables, these methods typically require redundant data to be sent back to the sink and not all of the aggregation functions could be executed precisely. In this paper, we modify an existing method to provide more accurate query answers and extend the lifetime of a wireless sensor network (WSN). According to our simulation, this method outperforms the existing method our approach modifies.

Abstract: Integrating and querying data from heterogeneous sources is a hot research topic in database research field. The emergence of the Semantic Web brings new paradigm shift of computing in data integration research where data is heterogeneous and distributed. To solve the problem this paper proposes a semantic-based approach. A Semantic-based Heterogeneous Relational Data Integration System, called SHRDIS, is presented. In this system, ontology is used as the mediated schema for the representation of the data source semantics. SPARQL queries over global schema are rewritten into local SQL queries that can be executed on heterogeneous relational databases. The architecture and implementation of SHRDIS is illustrated in detail. The experiment results show that the SHRDIS system has nice performance and scalability.

Abstract: In the domain of manufacture and logistics, Radio Frequency Identification (RFID) holds the promise of real-time identifying, locating, tracking and monitoring physical objects without line of sight due to an enhanced efficiency, accuracy, and preciseness of object identification, and can be used for a wide range of pervasive computing applications. To achieve these goals, RFID data has to be collected, filtered, and transformed into semantic application data. However, the amount of RFID data is huge. Therefore, it requires much time to extract valuable information from RFID data for object tracing. This paper specifically explores options for modeling and utilizing RFID data set by XML-encoding for tracking queries and path oriented queries. We then propose a method which translates the queries to SQL queries. Based on the XML-encoding scheme, we devise a storage scheme to process tracking queries and path oriented queries efficiently. Finally, we realize the method by programming in a software system for manufacture and logistics laboratory. The system shows that our approach can process the tracing or path queries efficiently.