Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
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: 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: 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: 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: Generic Fault Tolerant Software Architecture Reasoning and Customization
abstract: This paper proposes a novel heterogeneous software architecture GFTSA (Generic Fault Tolerant Software Architecture) which can guide the development of safety critical distributed systems. GFTSA incorporates an idealized fault tolerant component concept, and coordinated error recovery mechanism in the early system design phase. It can be reused in the high level model design of specific safety critical distributed systems with reliability requirements. To provide precise common idioms & patterns for the system designers, formal language Object-Z is used to specify GFTSA. Formal proofs based on Object-Z reasoning rules are constructed to demonstrate that the proposed GFTSA model can preserve significant fault tolerant properties. The inheritance & instantiation mechanisms of Object-Z can contribute to the customization of the GFTSA formal model. By analyzing the customization process, we also present a template of GFTSA, expressed in x-frames using the XVCL (XML-based Variant Configuration Language) methodology to make the customization process more direct & automatic. We use an LDAS (Line Direction Agreement System) case study to illustrate that GFTSA can guide the development of specific safety critical distributed systems

title: On Class Visualisation for High Dimensional Data: Exploring Scientific Data Sets
abstract: Parametric Embedding (PE) has recently been proposed as a general-purpose algorithm for class visualisation. It takes class posteriors produced by a mixture-based clustering algorithm and projects them in 2D for visualisation. However, although this fully modularised combination of objectives (clustering and projection) is attractive for its conceptual simplicity, in the case of high dimensional data, we show that a more optimal combination of these objectives can be achieved by integrating them both into a consistent probabilistic model. In this way, the projection step will fulfil a role of regularisation, guarding against the curse of dimensionality. As a result, the tradeoff between clustering and visualisation turns out to enhance the predictive abilities of the overall model. We present results on both synthetic data and two real-world high-dimensional data sets: observed spectra of early-type galaxies and gene expression arrays.

title: Latent semantic analysis for multiple-type interrelated data objects
abstract: Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.

title: A Data Mining Based Intrusion Detection Model
abstract: Intrusion Detection Systems (IDSs) have become a critical part of security systems. The goal of an intrusion detection system is to block intrusion effectively and accurately. However, the performance of IDS is not satisfying. In this paper, we study the issue of building a data mining based intrusion detection model to raise the detection performance. The key ideas are to use data mining techniques to discover consistent and useful patterns for intrusion and use the set of patterns to recognize intrusion. By applying statistics inference theory to this model, the patterns mined from a set of test data are effective to detect the attacks in the same category, and therefore can detect most novel attacks that are variants of known attacks.

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