Xu Lizhen

Research and Improvement of Load Balancing Algorithm in Distributed Sonar Data Stream Management System

In recent years, data stream technology has been widely used in the processing of sensor data. Sonar sensors produce high-speed streaming data with special characteristics of huge volume continuously. Therefore, general data stream management systems with limited memory and processor resources leads to inefficiencies in processing this kind of sonar data stream. However, in distributed data stream management system (DDSMS), continuous query can be reduced into several steps and processed on different processing nodes efficiently and effectively, so DDSMS is often adopted to deal with sonar stream data. Load balancing is a key technology of DDSMS, existing researches mainly focus on using computing load mainly caused by processing queries on the node as the key factor of load balancing algorithm, ignoring the load of transmitting huge volume data mainly caused by moving data among nodes. In this paper, the transmission load of sonar data is computed and used in an improved load balancing algorithm, which is proposed to solve the problem of load scheduling for sonar stream data processing under the distributed environment. The simulation verifies the importance of considering transmission load and shows a better performance of improved load balancing algorithm.

Web Service Description and Discovery Based on Semantic Model

A novel semantic model of Web service description and discovery was proposed through an extension for profile model of Web ontology language for services (OWL-S) in this paper. Similarity matching of Web services was implemented through computing weighted summation of semantic similarity value based on specific domain ontology and dynamical satisfy extent evaluation for quality of service (QoS). Experiments show that the provided semantic matching model is efficient.

Research of Cache Mechanism in Mobile Data Management

It can improve the performance of mobile data management by caching the frequently accessed and infrequently updated data items. In this paper, a cache replacement policy called Update-based Minimal Access Cost Replacement (UMACR) was proposed to remedy the defects of the existing cache replacement policies such as GDSF and OUR. A variety of factors such as sizes of data items, data access and update information were taken into account in UMACR. To facilitate the replacement policy, two enhanced cache access policies called Update-Server-based Poll-Each-Read (USBPER) and UpdateClient-based Call-Back (U2CB) were introduced to guarantee the consistency of data items. USBPER and U2CB remedied the defects of the existing cache access policies such as UPER and UCB by considering updates happening both at the server and clients. Conflict detection and handling was also implemented at the server. At last, we conducted a lot of simulation experiments of mobile data management, and the results demonstrate that the policies we proposed are effective enough, which lay a solid foundation for further research. Keywords-mobile data management; cache access policy; cache replacement policy

Research on High Availability Mechanism in Distributed Data Stream Management System

Existing research on high-availability(HA) of data stream systems mainly focused on restoring single node failure effectively, replacing the failed nodes with redundant backups to make the system go well. However, how to improve availability of DDSMS from the perspective of operator redundancy and multiple failures are paid less attention. In this paper, an improved HA mechanism based on operator redundancy in DDSMS is researched, a combined method for evaluating system overhead is proposed, and a way to choose the maximum redundancy is presented. The simulation verifies that this mechanism can provide a better synchronization service between the primary and the backup operator, and the chosen maximum redundancy can provide the maximum HA without affecting system performance.

Concept Similarity Computation for Ontology in the Automatic Question-Answering System

As the key point of the ontology research field, concept similarity computation has been widely used in the automatic question-answering systems. This paper firstly analyzes the existing concept similarity computation algorithm which is based on the semantic distance, and then proposes a new computation algorithm formula by modifying and adding some factors which can affect the accuracy of concept similarity computation. Finally, the experimental results show that the proposed algorithm offers better effectiveness for the concept similarity computation.

The Research of Key Technology in Flash-Based DBMS

As the capacity of flash memory increases and price drops, flash memory will compete more successfully with disk drives. It is thus not inconceivable to consider running a full database system on the flash-only computing platforms or running an embedded database system on the lightweight computing devices. In this paper, the characteristics of flash memory and the key points of designing a flash-based DBMS were analyzed. And a new design called In Block Logging(IBL) for flash memory based database servers was presented.This new design overcomes the limitations of flash memory such as high erase latency, and exploits unique features of flash memory to achieve the best attainable performance for flash-based database servers.

A Service Relevance Based Matching Algorithm on Grid Environment

This paper presents a semantic metadata description model for grid services, and furthermore, a service relevance based matching algorithm is proposed. Some reasonable parameters interpreting services characterizations are considered to advise matching process, whose weightiness and service relevance dramatically contribute to the decision-making of service matching degree. And whats more, mathing is secondly executed on the bases of experience adaptation of matching factors, which improves matching degree in gird service retrieve greatly.

A Granularity-Aware Parallel Aggregation Method for Data Streams

This paper focuses on the parallel aggregation processing of data streams based on the shared-nothing architecture. A novel granularity-aware parallel aggregating model is proposed. It employs parallel sampling and linear regression to describe the characteristics of the data quantity in the query window in order to determine the partition granularity of tuples, and utilizes equal depth histogram to implement partitioning. This method can avoid data skew and reduce communication cost. The experiment results on both synthetic data and actual data prove that the proposed method is efficient, practical and suitable for time-varying data streams processing.

Multidimensional data querying on tree-structured overlay

Multidimensional data query has been gaining much interest in database research communities in recent years, yet many of the existing studies focus mainly on centralized systems. A solution to querying in Peer-to-Peer(P2P) environment was proposed to achieve both low processing cost in terms of the number of peers accessed and search messages and balanced query loads among peers. The system is based on a balanced tree structured P2P network. By partitioning the query space intelligently, the amount of query forwarding is effectively controlled, and the number of peers involved and search messages are also limited. Dynamic load balancing can be achieved during space partitioning and query resolving. Extensive experiments confirm the effectiveness and scalability of our algorithms on P2P networks.