Jianzhong Li

Anonymizing Streaming Data for Privacy Protection

In many applications, transaction data arrive in the form of high speed data streams. These data contain a lot of information about customers, not just transactions, and thus have to be carefully managed to protect customers privacy. This paper presents a novel method called SKY (stream K-anon Ymtiy) to continuously facilitate k-anonymity on data streams. Experimental results show that SKY is efficient and effective.

Privacy protection on sliding window of data streams

In many applications, transaction data arrive in the form of high speed data streams. These data contain a lot of information about customers that needs to be carefully managed to protect customerspsila privacy. In this paper, we consider the problem of preserving customerpsilas privacy on the sliding window of transaction data streams. This problem is challenging because sliding window is updated frequently and rapidly. We propose a novel approach, SWAF (sliding window anonymization framework), to solve this problem by continuously facilitating k-anonymity on the sliding window. Three advantages make SWAF practical: (1) Small processing time for each tuple of data steam. (2) Small memory requirement. (3) Both privacy protection and utility of anonymized sliding window are carefully considered. Theoretical analysis and experimental results show that SWAF is efficient and effective.

A music data model and its application

A music data model, its query language and its application are proposed in this paper. Firstly, a music data model and its algebraic operations are given, which can be used to describe and manipulate musical data efficiently. Secondly, a structured query language on the model is proposed, which can be used to define and manage musical data. Finally, a digital music library, one of the applications of this model, is presented, which can be used to retrieve musical information, especially against musical instruments.

An effective wrapper architecture to heterogeneous data source

In this paper, we focus on the problem in information integration systems of obtaining data from an heterogeneous data source accurately and effectively. XML is used as data exchange format of the wrapper. We design the wrapper architecture based on the conversion and management of the views as the bridge from global schema to local schema of various data sources. Our wrapper has two main subsystems, the data extract subsystem and query executor subsystem. The former is for loading data for the cache in the mediator when changes more than threshold are detected, and the latter is for answering the query from the mediator. The architecture adapts to the data and schema change of the data source and could answer the query of the mediator effectively. Considering the wrapper may run in the environment without control, the process in the wrapper should be simple enough. The storage in the wrapper itself should be as small as possible and the storage of data source could be used. The detail of the module query rewrite, view management, query merge, result wrap and schema change detect are discussed. The behavior of the wrapper during the query process in the wrapper is discussed with a running example. The security strategy, especially in the distance that the wrapper runs in autonomic data source, is also introduced in this paper.

Labeling scheme and structural joins for graph-structured XML data

When XML documents are modeled as graphs, many challenging research issues arise. In particular, query processing for graph-structured XML data brings new challenges because traditional structural join methods cannot be directly applied. In this paper, we propose a labeling scheme for graph-structured XML data. With this labeling scheme, the reachability relationship of two nodes can be judged efficiently without accessing other nodes. Based on this labeling scheme, we design efficient structural join algorithms to evaluate reachability queries. Experiments show that our algorithms have high efficiency and good scalability.

Processing Sliding Window Join Aggregate in Continuous Queries over Data Streams

Processing continuous queries over unbounded streams require unbounded memory. A common solution to this issue is to restrict the range of continuous queries into a sliding window that contains the most recent data of data streams. Sliding window join aggregates are often-used queries in data stream applications. The processing method to date is to construct steaming binary operator tree and pipeline execute. This method consumes a great deal of memory in storing the sliding window join results, therefore it isn’t suitable for stream query processing. To handle this issue, we present a set of novel sliding window join aggregate operators and corresponding realized algorithms, which achieve memory-saving and efficient performance. Because the performances of proposed algorithms vary with the states of data streams, a scheduling strategy is also investigated to maximize the processing efficiency. The algorithms in this paper not only can process the complex sliding window join aggregate, but also can process the multi-way sliding window join aggregate.

Query processing in mobile ad hoc wireless networks

This paper investigates the query processing in mobile ad hoc wireless networks. A cost evaluation model for join operations is presented, which evaluates the primary parameters of wireless networks, including the size of the transmitted data, the transmission distance and the query cost at each local node. A join processing algorithm is also proposed in this paper. Given a multi-join query, our algorithm first decides how to transmit data among the referenced nodes, and then determine the optimal join order with minimal cost. Theoretical analysis and experiment results show that the proposed method is much more efficient.

Cell abstract indices for content-based approximate query processing in structured peer-to-peer data systems

In this paper, cell abstract indices are presented to process content-based approximate queries in structured P2P data systems. It can be used to search as few peers as possible but get as many returns satisfying users’ queries as possible on the guarantee of high autonomy of peers. Also, cell abstract indices have low system cost, can improve the query processing speed, and support very frequent updates and the set information publishing method. Simulation experiments are performed and analyzed to show the effectiveness of the proposed indices.

An Approach to Content-Based Approximate Query Processing in Peer-to-Peer Data Systems

In recent years there has been a significant interest in peer-to-peer (P2P) environments in the community of data management. However, almost all works, as far, focused on exact query processing in current P2P data systems. The autonomy of peers also doesn’t be considered enough. In addition, the system cost is very high because the information publishing method of shared data is based on each document instead of document set.

HIT-DML: A Novel Digital Music Library

The design and implementation of Harbin Institute of Technology-Digital Music Library (HIT-DML) is presented in this paper. HIT-DML adopts a novel framework which is inherently based on database systems. In this framework, musical data is structurally stored in the database, some algorithms of musical computation are implemented as algebraic mirco-operations in the database management system, and thus database technologies and multimedia technologies are combined seamlessly. A musical feature-matching algorithm and the appropriate dynamic index are also applied in HIT-DML. HIT-DML can retrieve musical information based on content, especially against different kinds of musical instruments.