Beyond streams and graphs: dynamic tensor analysis
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: 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: 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: 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: 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: 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: 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: 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 Reasoning Method for Timed CSP Based on Constraint Solving
abstract: Timed CSP extends CSP by introducing a capability to quantify temporal aspects of sequencing and synchronization. It is a powerful language to model real time reactive systems. However, there is no verification tool support for proving critical properties over systems modelled using Timed CSP. In this work, we construct a reasoning method using Constraint Logic Programming (CLP) as an underlying reasoning mechanism for Timed CSP. We start with encoding the semantics of Timed CSP in CLP, which allows a systematic translation of Timed CSP to CLP. Powerful constraint solver like CLP($\mathcal{R}$) is then used to prove traditional safety properties and beyond, e.g., reachability, deadlock-freeness, timewise refinement relationship, lower or upper bound of a time interval, etc. Counter-examples are generated when properties are not satisfied. Moreover, our method also handles useful extensions to Timed CSP. Finally, we demonstrate the effectiveness of our approach through case study of standard real time systems.

title: Query enrichment for web-query classification
abstract: Web-search queries are typically short and ambiguous. To classify these queries into certain target categories is a difficult but important problem. In this article, we present a new technique called query enrichment, which takes a short query and maps it to intermediate objects. Based on the collected intermediate objects, the query is then mapped to target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to the ACM Knowledge Discovery and Data Mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this article, we show that, despite the difficulty of an abundance of ambiguous queries and lack of training data, our query-enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best result for F1 and precision is 42.4% and 44.4%, respectively, which is 9.6% and 24.3% higher than those from the runner-ups, respectively.

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.