Streaming Pattern Discovery in Multiple Time-Series
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: 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: 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: 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: 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: 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.

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: Latent Friend Mining from Blog Data
abstract: The rapid growth of blog (also known as "weblog") data provides a rich resource for social community mining. In this paper, we put forward a novel research problem of mining the latent friends of bloggers based on the contents of their blog entries. Latent friends are defined in this paper as people who share the similar topic distribution in their blogs. These people may not actually know each other, but they have the interest and potential to find each other out. Three approaches are designed for latent friend detection. The first one, called cosine similarity-based method, determines the similarity between bloggers by calculating the cosine similarity between the contents of the blogs. The second approach, known as topic-based method, is based on the discovery of latent topics using a latent topic model and then calculating the similarity at the topic level. The third one is two-level similarity-based, which is conducted in two stages. In the first stage, an existing topic hierarchy is exploited to build a topic distribution for a blogger. Then, in the second stage, a detailed similarity comparison is conducted for bloggers that are close in interest to each other which are discovered in the first stage. Our experimental results show that both the topic-based and two-level similarity-based methods work well, and the last approach performs much better than the first two. In this paper, we give a detailed analysis of the advantages and disadvantages of different approaches.

title: Bounding Completion Times of Jobs with Arbitrary Release Times, Variable Execution Times and Resource Sharing
abstract: The workload of many real time systems can be characterized as a set of preemptable jobs with linear precedence constraints. Typically their execution times are only known to lie within a range of values. In addition, jobs share resources and access to the resources must be synchronized to ensure the integrity of the system. The paper is concerned with the schedulability of such jobs when scheduled on a priority driven basis. It describes three algorithms for computing upper bounds on the completion times of jobs that have arbitrary release times and priorities. The first two are simple but do not yield sufficiently tight bounds, while the last one yields the tightest bounds but has the greatest complexity.

title: Video object cut and paste
abstract: In this paper, we present a system for cutting a moving object out from a video clip. The cutout object sequence can be pasted onto another video or a background image. To achieve this, we first apply a new 3D graph cut based segmentation approach on the spatial-temporal video volume. Our algorithm partitions watershed presegmentation regions into foreground and background while preserving temporal coherence. Then, the initial segmentation result is refined locally. Given two frames in the video sequence, we specify two respective windows of interest which are then tracked using a bi-directional feature tracking algorithm. For each frame in between these two given frames, the segmentation in each tracked window is refined using a 2D graph cut that utilizes a local color model. Moreover, we provide brush tools for the user to control the object boundary precisely wherever needed. Based on the accurate binary segmentation result, we apply coherent matting to extract the alpha mattes and foreground colors of the object.