Tiancheng Zhang

Adaptive correlation analysis in stream time series with sliding windows

Correlation analysis is a very useful technique for similarity search in the field of data stream mining. The traditional method is not suitable for real time processing especially when the amount of stream sequences is very large. In this paper, we propose HBR (Hierarchical Boolean Representation), a novel technique for correlation analysis in stream time series. The original stream sequences are transformed into the Macro-Boolean series and the Micro-Boolean series successively, and the candidate correlation set can be easily obtained by simple bit operations. With huge amount of stream series, this method can quickly get the correlation pairs of series efficiently by reducing complicated calculation in a little space. Meanwhile, this approach can update the Boolean series incrementally with very low cost and adjust some important coefficients adaptively by the stream feature. The experimental evaluations show that HBR has excellent computation complexity with high accuracy.

Boolean representation based data-adaptive correlation analysis over time series streams

Correlation analysis is a basic problem in the field of data stream mining. Typical approaches add sliding window to data streams to get the recent results, but the window length defined by users is always fixed which is not suitable for the changing stream environment. We propose a Boolean representation based data-adaptive method for correlation analysis among a large number of time series streams. The periodical trends of each stream series to are monitored to choose the most suitable window size and group the series with the same trends together. Instead of adopting complex pair-wise calculation, we can also quickly get the correlation pairs of series at the optimal window sizes. All the processing is realized by simple Boolean operations. Both the theory analysis and the experimental evaluations show that our method has good computation efficiency with high accuracy.

A novel approach for mining multiple data streams based on lag correlation

Correlation analysis is a key problem for data stream analysis. In this paper, we propose a correlation analysis method for multiple dimensional data streams, which is based on the Boolean lag representation and the PCA (Principal Component Analysis). Firstly, the raw stream sequence is transformed into the Boolean sequence. By the correlation analysis of Boolean sequences, we can easily find the sequence pairs with lag correlations by means of simple bit operations. Secondly, we compute the lag time and synchronize the multiple dimensional data stream. Thirdly, the PCA method is deployed to reduce the multiple data streams, and we can reconstruct the data streams by a few principal components. The experimental evaluations show that the method has high computation performance with high accuracy.

Research on Event Prediction Algorithm Based on Event Sequence Semantic

Event prediction in event stream is an important problem in temporal data mining. However, existing event prediction algorithms are based on string prediction in which a character represents an event or an event type, do not take into account event sequence semantic and can not predict for infrequent event sequences. In this paper, an event prediction algorithm based on event sequence semantic called SVClustering-SVR is proposed to predict probability of target event occurrence in event stream in appointed interval. We build a vector structure called semantic vector to express event sequence semantic, and then utilize the attributes of standardizing semantic vector and confidence of rule which is generated by event sequences and target event to form samples space. Finally, we use Support Vector Regression (SVR) to build prediction model. To improve the accuracy of prediction, we also define semantic distance between event sequences and cluster semantic vectors. SVClustering-SVR algorithm can predict for infrequent event sequences and those not appeared in training set. Experimental results show the effectiveness of SVClustering-SVR algorithm.

Correlation Analysis Based on Hierarchical Boolean Representation over Time Series Data Streams

Correlation analysis is a basic problem in the field of data stream mining. Traditional method is not suitable for real time processing with huge amount of stream data. We propose a hierarchical Boolean representation method for correlation analysis among time series data streams. The original streaming series are transformed into the Macro- Boolean series and then the Micro-Boolean series successively, and the candidate can be easily gained by simple bit operations. With huge amount of streaming series, this method can quickly get the correlation pairs of series in an efficient way by reducing huge calculation in a little space The experimental evaluations show that our method has better computation complexity with high accuracy.

Algebra for Parallel XQuery Processing

As XML becomes the standard of data presentation and information exchange, how to efficiently query information from XML documents becomes a hot topic. However, for larger XML documents and complicated XQueries, the performance of query processing which executes in a single node can seldom meet the needs of users. In this paper, algebra PPXA (Pure Parallel XQuery Algebra) is proposed to support parallel processing for XQuery statements. Based on the Algebra, a strategy for query plan decomposition is proposed for complex path queries and Twig queries. Then, we propose three optimization algorithms based on PPXA. The logical parallel execution plan is optimized by rules on operators, which reduce the local query execution costs. We implement the algebra and the query decomposition strategy in a native XML database system PureXBase. The experimental results show that it supports the XQuery parallel query processing effectively, and can significantly improve the efficiency of query processing.

A Simulation Platform for RFID Application Deployment Supporting Multiple Scenarios

Radio Frequency Identification (RFID) poses multiple advantages over traditional barcodes, such as hands-off detection, longer read range and more data storage. In addition, the declining cost of RFID systems along with improved sensitivity and durability nowadays has increased its usage potential in a variety of domains such as logistical, planning and supply chain process. However, the deployment of RFID facilities in real-world scenario always takes time and money. Once some significant design weaknesses appear, the facilities must be deployed all over again. In this paper, we present an RFID simulation platform, RFIDSim, which supports users to build their own virtual scenario and deploy RFID facilities in it instead. This simulation platform, which relies on a discrete event simulator, is designed to implement part of ISO 18000-6C communication protocol and support path loss, backscatter, capture and tag mobility models. Besides, the reader models are programable by using a special language so that users can adjust the readers into different applications. All the data collected during the simulation would be stored in the database for users to judge if a certain deployment is fairly appropriate.

Energy-Efficient and Smoothing-Sensitive Curve Recovery of Sensing Physical World

In recent years, sensing networks are widely used in the application of real-time monitoring. The change process of physical word is smoothing and continuous, but the sensing devices can only obtain the discrete data points. It is likely to lose the key points and distort the true curve if the discrete points are used simply to describe the physical world. Therefore, how to recover the approximate curve of physical world becomes a problem to be solved urgently. Based on this, an energy-efficient and smoothing-sensitive high-precision curve recovery algorithm for the sensing networks is proposed. Firstly, we recover the curve of physical world based on the existing physical-world-aware data acquisition algorithms preliminarily. And then a curve smoothing algorithm is proposed in order to acquire more key points (the inflexions are mainly considered in this paper) information which helps users better understand the change process of monitored physical world intuitively. Secondly, we propose an energy-efficient data source selection algorithm with residual energy of each data source and spatial correlation under consideration simultaneously. We select part of data sources to transmit data, maximize the lifetime of sensing network and minimize the error between the approximate curve and physical world. Finally, the effectiveness of our algorithms is verified by abundant experiments using both real and simulated data.

Efficient Keyword Search for SLCA in Parallel XML Databases

Keyword search is a wildly popular way for querying XML document. However, the increasing volume of XML data poses new challenges to keyword search processing. Parallel database is an efficient solution for this problem. In this paper, we study the problem of effective keyword search for SLCA (Smallest lower common ancestor) in parallel XML databases. We propose two efficient algorithm SONB (Scan once with no buffer) and MSOP (Merge strategy based on ordered partition) to compute the SLCA efficiently in the parallel environment. We have performed an extensive experimental study and the results show that our proposed approach achieves high efficiency for the keyword search.

Research and implementation of an RFID object tracking system simulation platform

Radio Frequency Identification (RFID) has been the focus of research for its valuable application in space-time information query. Because of performing experiments in real RFID application systems is difficult, this paper proposes an RFID object tracking system simulation platform to assist the researching of RFID uncertain data management and space-time information query. After researching the simulation models of the reader, tag and radio propagation in physical layer and logical layer, this paper presents the simulation strategy which is combined by discrete event scheduling and activity scanning. The platform is implemented by three layers based on Eclipse RCP and GEF. The system can be easily extended just by a new plug-in developed by the user. At last, a basic object tracking test is presented to prove that it can reproduce RFID application scenes efficiently and can assist the research in this field.