Analysis of predictive spatio-temporal queries
Given a set of objects <i>S</i>, a spatio-temporal <i>window query q</i> retrieves the objects of <i>S</i> that will intersect the window during the (future) interval <i>q</i><sub><i>T</i></sub>. A <i>nearest neighbor query q</i> retrieves the objects of <i>S</i> closest to <i>q</i> during <i>q</i><sub><i>T</i></sub>. Given a threshold <i>d</i>, a spatio-temporal <i>join</i> retrieves the pairs of objects from two datasets that will come within distance <i>d</i> from each other during <i>q</i><sub><i>T</i></sub>. In this article, we present probabilistic cost models that estimate the <i>selectivity</i> of spatio-temporal window queries and joins, and the <i>expected distance</i> between a query and its nearest neighbor(s). Our models capture any query/object mobility combination (moving queries, moving objects or both) and any data type (points and rectangles) in arbitrary dimensionality. In addition, we develop specialized spatio-temporal histograms, which take into account both location and velocity information, and can be incrementally maintained. Extensive performance evaluation verifies that the proposed techniques produce highly accurate estimation on both uniform and non-uniform data.

title: Relevance search and anomaly detection in bipartite graphs
abstract: 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 (relevance search), and 2) finding nodes connecting irrelevant nodes (anomaly detection). And we propose algorithms to compute the relevance score for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify anomalies, using relevance scores. We evaluate the quality of relevance search 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: Neighborhood formation and anomaly detection in bipartite graphs
abstract: 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: Hiding in the Crowd: Privacy Preservation on Evolving Streams through Correlation Tracking
abstract: We address the problem of preserving privacy in streams, which has received surprisingly limited attention. For static data, a well-studied and widely used approach is based on random perturbation of the data values. However, streams pose additional challenges. First, analysis of the data has to be performed incrementally, using limited processing time and buffer space, making batch approaches unsuitable. Second, the characteristics of streams evolve over time. Consequently, approaches based on global analysis of the data are not adequate. We show that it is possible to efficiently and effectively track the correlation and autocorrelation structure of multivariate streams and leverage it to add noise which maximally preserves privacy, in the sense that it is very hard to remove. Our techniques achieve much better results than previous static, global approaches, while requiring limited processing time and memory. We provide both a mathematical analysis and experimental evaluation on real data to validate the correctness, efficiency, and effectiveness of our algorithms.

title: Beyond streams and graphs: dynamic tensor analysis
abstract: How do we find patterns in author-keyword associations, evolving over time? Or in Data Cubes, with product-branch-customer sales information? Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks and many more. However, they have only two orders, like author and keyword, in the above example.We propose to envision such higher order data as tensors,and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic tensor analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully so that it is (a) scalable, (b) space efficient (it does not need to store the past) and (c) fully automatic with no need for user defined parameters. Moreover, we propose STA, a streaming tensor analysis method, which provides a fast, streaming approximation to DTA.We implemented all our methods, and applied them in two real settings, namely, anomaly detection and multi-way latent semantic indexing. We used two real, large datasets, one on network flow data (100GB over 1 month) and one from DBLP (200MB over 25 years). Our experiments show that our methods are fast, accurate and that they find interesting patterns and outliers on the real datasets.

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: 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: Design synthesis from interaction and state-based specifications
abstract: Interaction-based and state-based modeling are two complementary approaches of behavior modeling. The former focuses on global interactions between system components. The latter concentrates on the internal states of individual components. Both approaches have been proven useful in practice. One challenging and important research objective is to combine the modeling power of both effectively and then use the combination as the basis for automatic design synthesis. We present a combination of interaction-based and state-based modeling, namely, live sequence charts and Z, for system specification. We then propose a way of generating distributed design from the combinations. Our approach handles systems with intensive interactive behaviors as well as complex state structures

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: CubeSVD: a novel approach to personalized Web search
abstract: As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users' information needs. This paper focuses on utilizing clickthrough data to improve Web search. Since millions of searches are conducted everyday, a search engine accumulates a large volume of clickthrough data, which records who submits queries and which pages he/she clicks on. The clickthrough data is highly sparse and contains different types of objects (user, query and Web page), and the relationships among these objects are also very complicated. By performing analysis on these data, we attempt to discover Web users' interests and the patterns that users locate information. In this paper, a novel approach CubeSVD is proposed to improve Web search. The clickthrough data is represented by a 3-order tensor, on which we perform 3-mode analysis using the higher-order singular value decomposition technique to automatically capture the latent factors that govern the relations among these multi-type objects: users, queries and Web pages. A tensor reconstructed based on the CubeSVD analysis reflects both the observed interactions among these objects and the implicit associations among them. Therefore, Web search activities can be carried out based on CubeSVD analysis. Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods.

title: A Risk-Sensitive Intrusion Detection Model
abstract: Intrusion detection systems (IDSs) must meet the security goals while minimizing risks of wrong detections. In this paper, we study the issue of building a risk-sensitive intrusion detection model. To determinate whether a system calls sequence is normal or not, we consider not only the probability of this sequence belonging to normal sequences set or intrusion sequences set, but also the risk of a false detection. We define the risk model to formulate the expected risk of an intrusion detection decision, and present risk-sensitive machine learning techniques that can produce detection model to minimize the risks of false negatives and false positives. Meanwhile, this model is a hybrid model that combines misuse intrusion detection and anomaly intrusion detection. To achieve a satisfying performance, some techniques are applied to extend this model.