Local Correlation Tracking in Time Series
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: InteMon: intelligent system monitoring on large clusters
abstract: InteMon is a prototype monitoring and mining system for large clusters. Currently, it monitors over 100 hosts of a prototype data center at CMU. It uses the SNMP protocol and it stores the monitoring data in an mySQL database. Then, it allows for visualization of the time-series data using a JSP web-based frontend interface for users.What sets it apart from other cluster monitoring systems is its ability to automatically analyze the monitoring data in real time and alert the users for potential anomalies. It uses state of the art stream mining methods, it has a sophisticated definition of anomalies (broken correlations among input streams), and it can also pinpoint the reason of the anomaly. InteMon has a user-friendly GUI, it allows the users to perform interactive mining tasks, and it is fully operational.

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: 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: 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: Selectivity estimation for predictive spatio-temporal queries
abstract: We propose a cost model for selectivity estimation of predictive spatio-temporal window queries. Initially, we focus on uniform data proposing formulae that capture both points and rectangles, and any type of object/query mobility combination (i.e., dynamic objects, dynamic queries or both). Then, we apply the model to nonuniform datasets by introducing spatio-temporal histograms, which in addition to the spatial, also consider the velocity distributions during partitioning. The advantages of our techniques are (i) high accuracy (1-2 orders of magnitude lower error than previous techniques), (ii) ability to handle all query types, and (iii) efficient handling of updates.

title: Realizing Live Sequence Charts in SystemVerilog
abstract: The design of an embedded control system starts with an investigation of properties and behaviors of the process evolving within its environment, and an analysis of the requirement for its safety performance. In early stages, system requirements are often specified as scenarios of behavior using sequence charts for different use cases. This specification must be precise, intuitive and expressive enough to capture different aspects of embedded control systems. As a rather rich and useful extension to the classical message sequence charts, live sequence charts (LSC), which provide a rich collection of constructs for specifying both possible and mandatory behaviors, are very suitable for designing an embedded control system. However, it is not a trivial task to realize a high-level design model in executable program codes effectively and correctly. This paper tackles the challenging task by providing a mapping algorithm to automatically synthesize SystemVerilog programs from given LSC specifications.

title: A PVS Approach to Verifying ORA-SS Data Models
abstract: The rapid growth of the World Wide Web has resulted in a dramatic increase in semistructured data usage. This creates a growing need for ensuring consistency of the data especially when applications or databases change the schema of semistructured data. In this paper, we demonstrate an approach to formally define and verify the ORA-SS data model for semistructured data design. A mathematical semantics for the ORA-SS notation is defined using the PVS formal language. With this semantics, it is possible to perform automated verification via the PVS theorem prover to identify the inconsistencies in designing ORA-SS data models. The verification is based on criteria for semistructured data ve rification at both schema and data instance levels.

title: Picture Collage
abstract: In this paper, we address a novel problem of automatically creating a picture collage from a group of images. Picture collage is a kind of visual image summary-to arrange all input images on a given canvas, allowing overlay, to maximize visible visual information. We formulate the picture collage creation problem in a conditional random field model, which integrates image salience, canvas constraint, natural preference, and user interaction. Each image is represented by a group of weighted rectangles, which indicate the salient regions. Then picture collage is resolved by minimizing the energy, guided by the constraints. A two-step optimization method is proposed. First, a quick initialization algorithm based on the proposed 1D collage method is presented. Second, a very efficient Markov chain Monte Carlo method is designed for the refined optimization. We also integrate user interaction in the formulation and optimization to obtain an interactive collage reflecting personalized preference. Visual and quantitative experimental evaluations indicate the efficiency of the proposed collage creation technique.

title: A practical system of keyphrase extraction for web pages
abstract: Keyphrases can be used to facilitate Web users grasping the main topic(s) of a Web page. We present a practical system of automatic keyphrase extraction for Web pages. In this system, a regression model was first trained based on a set of human-labeled documents. Then it was used to extract keyphrases from new pages automatically. This paper makes three contributions. First, the structure information in a Web page was investigated for keyphrase extraction task. Second, the query log data associated with a Web page collected by a search engine server were used to help keyphrase extraction. Third, a method was put forward in this paper in order to evaluate the similarity of phrases.

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.