Neighborhood formation and anomaly detection in bipartite graphs
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.

title: Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
abstract: Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, tensor streams, and an effective algorithmic framework, window-based tensor analysis (WTA). Two variations of WTA, independent- window tensor analysis (IW) and moving-window tensor analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, multi-aspect correlation analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.

title: Local Correlation Tracking in Time Series
abstract: We address the problem of capturing and tracking local correlations among time evolving time series. Our approach is based on comparing the local auto-covariance matrices (via their spectral decompositions) of each series and generalizes the notion of linear cross-correlation. In this way, it is possible to concisely capture a wide variety of local patterns or trends. Our method produces a general similarity score, which evolves over time, and accurately reflects the changing relationships. Finally, it can also be estimated incrementally, in a streaming setting. We demonstrate its usefulness, robustness and efficiency on a wide range of real datasets.

title: Spatio-temporal join selectivity
abstract: Given two sets S1, S2 of moving objects, a future timestamp tq, and a distance threshold d, a spatio-temporal join retrieves all pairs of objects that are within distance d at tq. The selectivity of a join equals the number of retrieved pairs divided by the cardinality of the Cartesian product S1 × S2. This paper develops a model for spatio-temporal join selectivity estimation based on rigorous probabilistic analysis, and reveals the factors that affect the selectivity. Initially, we solve the problem for ID (point and rectangle) objects whose location and velocities distribute uniformly, and then extend the results to multi-dimensional spaces. Finally, we deal with non-uniform distributions using a specialized spatio-temporal histogram. Extensive experiments confirm that the proposed formulae are highly accurate (average error below 10%).

title: Online latent variable detection in sensor networks
abstract: Sensor networks attract increasing interest, for a broad range of applications. Given a sensor network, one key issue becomes how to utilize it efficiently and effectively. In particular, how can we detect the underlying correlations (latent variables) among many co-evolving sensor measurements? Can we do it incrementally? We present a system that can (1) collect the measurements from the real wireless sensors; (2) process them in real-time; and (3) determine the correlations (latent variables) among the sensor streams on the fly.

title: Streaming Pattern Discovery in Multiple Time-Series
abstract: In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Time-series). Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection. It can do this quickly, with no buffering of stream values and without comparing pairs of streams. Moreover, it is any-time, single pass, and it dynamically detects changes. The discovered trends can also be used to immediately spot potential anomalies, to do efficient forecasting and, more generally, to dramatically simplify further data processing. Our experimental evaluation and case studies show that SPIRIT can incrementally capture correlations and discover trends, efficiently and effectively.

title: Text Classification Improved through Automatically Extracted Sequences
abstract: We propose to use the n-multigram model to help the automatic text classification task. This model could automatically discover the latent semantic sequences contained in the document set of each category. Based on the n-multigram model and the n-gram language model, we put forward two text classification algorithms. The experiments on RCV1 show that our proposed algorithm based on n-multigram model can achieve the similar classification performance compared with the one based on n-gram model. However, the model size of our algorithm is only 4.21% of the latter one. Another proposed algorithm based on the combination of nmultigram model and n-gram model improves the micro- F1 and macro-F1 values by 3.5% and 4.5% respectively which support the validity of our approach.

title: Predicting Large Population Data Cumulative Match Characteristic Performance from Small Population Data
abstract: Given a biometric feature-space, in this paper we present a method to predict cumulative match characteristic (CMC) curve performance for a large population of individuals using a significantly smaller population to make the prediction. This is achieved by mathematically modelling the CMC curve. For a given biometric technique that extracts measurements of individuals to be used for identification, the CMC curve shows the probability of recognizing that individual within a database of measurements that are extracted from multiple individuals. As the number of individuals in the database increase, the probabilities displayed on the CMC curve decrease, which indicate the decreasing ability of the biometric technique to recognize individuals. Our mathematical model replicates this effect, and allows us to predict the identification performance of a technique as more individuals are added without physically needing to extract measurements from more individuals.

title: Reasoning about semantic Web in Isabelle/HOL
abstract: Semantic Web is regarded as the next generation of the World Wide Web. It provides not only the structure of the Web but also meaningful semantics for the information presented. To make semantic Web services understandable for distributed agents, formal definitions of the ontologies and their consistencies are essential. However, the existing tools for reasoning about semantic Web ontologies are still primitive. We believe that mature software engineering tools, such as theorem provers, can contribute to the reasoning phase. In this paper, we present an approach of encoding the semantic Web ontology (DAML+OIL) into the generic theorem prover Isabelle/HOL for automatic reasoning. Furthermore, a translation tool was developed to transform semantic Web ontologies into their extended Isabelle theories. With additional intermediate lemmas, Isabelle can be used to perform both subsumption (class) level and instantiation (instance) level reasoning of the semantic Web ontologies.

title: Algorithmic Design Using Object-Z for Twig XML Queries Evaluation
abstract: Web technologies based on XML, e.g. Semantic Web and Web Services, facilitate Web automation and universally accessible content. One of the key factors for the success of XML-based technologies is of finding an efficient query evaluation algorithm for XML-based data models. An XML twig query is a complex selection predicate on both structure and content of a labelled XML document. Several novel twig query evaluation algorithms have been proposed recently. However, these algorithms are difficult to understand and hence implement due to high complexity. In this work, we present an algorithmic design for XML queries evaluation system using Object-Z. An Object-Z specification is developed to give a concise and logical description of the XML data model and the twig queries. It makes the twig query evaluation straight-forward, and allows different evaluation algorithms to be constructed easily and independently.

title: Timed Patterns: TCOZ to Timed Automata
abstract: The integrated logic-based modeling language, Timed Communicating Object Z (TCOZ), is well suited for presenting complete and coherent requirement models for complex real-time systems. However, the challenge is how to verify the TCOZ models with tool support, especially for analyzing timing properties. Specialized graph-based modeling technique, Timed Automata (TA), has powerful mechanisms for designing real-time models using multiple clocks and has well developed automatic tool support. One weakness of TA is the lack of high level composable graphical patterns to support systematic designs for complex systems. The investigation of possible links between TCOZ and TA may benefit both techniques. For TCOZ, TA’s tool support can be reused to check timing properties. For TA, a set of composable graphical patterns can be defined based on the semantics of the TCOZ constructs, so that those patterns can be re-used in a generic way. This paper firstly defines the composable TA graphical patterns, and then presents sound transformation rules and a tool for projecting TCOZ specifications into TA. A case study of a railroad crossing system is demonstrated.