A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) components to form a situation-integrated analytical system that may respond intelligently to dynamic changes of the real-world situations. CPS claims many promising applications, such as traffic observation, battlefield surveillance and sensor-network based monitoring. One important research topic in CPS is about the atypical event analysis, i.e., retrieving the events from large amount of data and analyzing them with spatial, temporal and other multi-dimensional information. Many traditional approaches are not feasible for such analysis since they use numeric measures and cannot describe the complex atypical events. In this study, we propose a new model of atypical cluster to effectively represent those events and efficiently retrieve them from massive data. The micro-cluster is designed to summarize individual events, and the macro-cluster is used to integrate the information from multiple event. To facilitate scalable, flexible and online analysis, the concept of significant cluster is defined and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real datasets with the size of more than 50 GB, the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.

Performance characterization of applications' hardware behavior is essential for making the best use of available hardware resources. Modern architectures offer access to many hardware events that are capable of providing information to reveal architectural performance bottlenecks throughout the core and memory hierarchy. These events can provide programmers with unique and powerful insights into the causes of the resource bottlenecks in their applications. However, interpreting these events has been a significant challenge. We present an automated system that uses machine learning to identify an application's performance problems. Our system provides programmers with insights about the performance of their applications while shielding them from the onerous task of digesting hardware events. It uses a decision tree algorithm, random forests on our micro-benchmarks to fingerprint the performance problems. Our system divides a profiled application into functions and automatically classifies each function by the dominant hardware resource bottlenecks. Using the classifications from the hotspot functions, we were able to achieve an average speedup of 1.73 from three applications in the PARSEC benchmark suite. Our system provides programmers with a guideline of where, what, and how to fix the detected performance problems in applications, which would have otherwise required considerable architectural knowledge.

In the past, there have been dozens of studies on automatic authorship classification, and many of these studies concluded that the writing style is one of the best indicators for original authorship. From among the hundreds of features which were developed, syntactic features were best able to reflect an author's writing style. However, due to the high computational complexity for extracting and computing syntactic features, only simple variations of basic syntactic features such as function words, POS(Part of Speech) tags, and rewrite rules were considered. In this paper, we propose a new feature set of k-embedded-edge subtree patterns that holds more syntactic information than previous feature sets. We also propose a novel approach to directly mining them from a given set of syntactic trees. We show that this approach reduces the computational burden of using complex syntactic structures as the feature set. Comprehensive experiments on real-world datasets demonstrate that our approach is reliable and more accurate than previous studies.

In this paper we introduce a new type of pattern -- a flipping correlation pattern. The flipping patterns are obtained from contrasting the correlations between items at different levels of abstraction. They represent surprising correlations, both positive and negative, which are specific for a given abstraction level, and which "flip" from positive to negative and vice versa when items are generalized to a higher level of abstraction. We design an efficient algorithm for finding flipping correlations, the Flipper algorithm, which outperforms naïve pattern mining methods by several orders of magnitude. We apply Flipper to real-life datasets and show that the discovered patterns are non-redundant, surprising and actionable. Flipper finds strong contrasting correlations in itemsets with low-to-medium support, while existing techniques cannot handle the pattern discovery in this frequency range.

A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational)components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustworthiness analysis of the observed data: Due to technology limitations and environmental influences, the CPS data are inherently noisy that may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this paper, we propose a method called Tru-Alarm which finds out trustworthy alarms and increases the feasibility of CPS. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inferences based on linked information in the graph. Extensive experiments show that Tru-Alarm filters out noises and false information efficiently and guarantees not missing any meaningful alarms.

In this paper, we propose GAD (General Activity Detection) for fast clustering on large scale data. Within this framework we design a set of algorithms for different scenarios: (1) Exact GAD algorithm E-GAD, which is much faster than K-Means and gets the same clustering result. (2) Approximate GAD algorithms with different assumptions, which are faster than E-GAD while achieving different degrees of approximation. (3) GAD based algorithms to handle the “large clusters” problem which appears in many large scale clustering applications. Two existing activity detection algorithms GT and CGAUTC are special cases under the framework. The most important contribution of our work is that the framework is the general solution to exploit activity detection for fast clustering in both exact and approximate senarios, and our proposed algorithms within the framework can achieve very high speed. Extensive experiments have been conducted on several large datasets from various real world applications; the results show that our proposed algorithms are effective and efficient.

For the past decade, the need of multimedia mining has increased tremendously, especially in image data due to inexpensive digital technologies and fast mounting of image data. In this paper, we, first, show an algorithm, SpIBag (Spatial Item Bag Mining), which discovers frequent spatial patterns in images. Due to the properties of image data, SpIBag considers a bag of items together with a spatial information as a pattern which persists over geometrical transformations, such as scaling, translation, and rotation. Then, based on SpIBag, we propose SpaRClus (Spatial Relationship Pattern-Based Hierarchical Clustering) to cluster image data. Our performance study shows that the method is effective and efficient.

Ranking has been popularly used for intelligent data retrieval in both database and machine learning communities. Recently, there were studies on integrating these two approaches to support soft queries, based on a user's sense of relevance and preference, for ranking with numerical attributes. However, in real life, it is desirable to use categorical attributes together with numerical ones in ranking. For example, when buying a car, categorical attributes, such as make, model, color, and equipments, are considered as significant factors as numerical attributes, such as price and year. Meanwhile, users often do not have sufficient domain knowledge at formulating an effective selection query over categories, whereas rank formulation is even more challenging as categories have no inherent ordering. In this paper, we propose a framework PerRank (Personalized Ranking with Categorical and Numerical Attributes) to support personalized ranking with both categorical and numerical attributes for soft queries. For an efficient computation, we developed an algorithm CAC (Clustering-based Attribute Construction) which makes use of a clustering method. Extensive experiments show CAC is effective and efficient at supporting ranking with both categorical and numerical attributes for soft queries.

With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is automated identification of suspicious movements. Due to the sheer volume of data associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features. In this study, we propose a new framework named ROAM (Rule- and Motif-based Anomaly Detection in Moving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments called motifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rule-based classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. We implemented ROAM and tested its components under a variety of conditions. Our experiments show that the system is efficient and effective at detecting abnormal moving objects.