9097817
Reasoning and Merging in XML Data Integration
In this paper, we study the problem of making use of target constraints to integrate XML data from different sources under a target schema. We recognize that target constraints are necessary in data integration, as the constraints are essential part of data semantics, and should be satisfied by integrated data. When integrating data from multiple data sources with overlapping data, constraints can express data merging rules at the target as well. We give a general constraint model for XML to express target constraints, which extends the relational equality-generating and tuple- generating dependencies. We provide a chase method to reason about data in the integrated XML document based on target constraints, by inferring data values not given explicitly, and inserting new subtrees as necessary. Singleton and key constraints are used to uniquely specify a certain entity, as a rule for data merging in the integration.
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
14792668
Personalization as a service: the architecture and a case study
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
10409272
Repairing Inconsistent XML Documents
XML document may contain inconsistencies that violate predefined integrity constraints, and there are two basic concepts for this problem: Repair is the data consistent with the integrity constraints, and also minimally differs from the original one. Consistent data is the data common for every possible repair. In this paper, first we give a general constraint model for XML, which can express functional dependencies, keys and multivalued dependencies. Next we provide a repair framework for inconsistent XML document with three basic update operations: node insertion, node deletion and value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase process to generate repairs, and prove some important properties of the chase process. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data.
30881426
Constraint-Based Graph Mining in Large Database
Currently, constraints are increasingly considered as a kind of means of user- or expert-control for filtering those unsatisfied and redundant patterns rapidly during the web mining process. Recent work has highlighted the importance of constraint-based mining paradigm in the context of frequent itemsets, sequences, and many other interesting patterns in large database. However, it is still not clear how to push various constraints systematically into graph mining process. In this paper, we categorize various graph-based constraints into several major classes and develop a framework CabGin (i.e. C onstr a int- b ased G raph M in ing) to push them into mining process by their categories. Non-monotonic aggregates like average also can be pushed into CabGin with minor revision. Experimental results show that CabGin can prunes a large search space effectively by pushing graph-based constraints into mining process.
207162632
Utility-based anonymization for privacy preservation with less information loss
Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often used for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.
 In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
6713943
Human motion estimation from a reduced marker set
Motion capture data from human subjects exhibits considerable redundancy. In this paper, we propose novel methods for exploiting this redundancy. In particular, we set out to find a subset of motion-capture markers that are able to provide fast and high-quality predictions of the remaining markers. We then develop a model that uses this reduced marker set to predict the others. We demonstrate that this subset of original markers is sufficient to capture subtle variations in human motion.We take a data-driven modeling approach to learn piecewise local linear models from a marker-based training set. We first divide motion sequences into segments of low dimensionality. We then retrieve a feature vector from each of the motion segments and use these feature vectors as modeling primitives to cluster the segments into a hierarchy of local linear models via a divisive clustering method. The selection of an appropriate linear model for reconstruction of a full-body pose is determined automatically via a classifier driven by a reduced marker set. After offline training, our method can quickly reconstruct full-body human motion using a reduced marker set without storing and searching the large database. We also demonstrate our method's ability to generalize over a variety of motions from multiple subjects.
6079003
Three-Tier Clustering: An Online Citation Clustering System
In this paper, we present a three tier clustering method where data objects are described by a number of feature dimensions. Using the approach, similarity along each feature dimension of objects are first computed. The inter-objects similarity are then computed from inter-feature-dimension similarity using a Bayesian multi-causal model. Objects are finally clustered based on the computed similarity. An online citation entry clustering system was built using the approach. It accepts user queries in the form of name of authors. Such queries are sent to citation/bibliography search engines. The returned entries are clustered based on feature dimensions such as authors, title, place of publication, etc. After clustering, entries from different authors with the similar name form different clusters, that are presented to the user. Preliminary experiment results indicated the effectiveness of the proposed clustering approach. The architecture of three-tire clustering framework, feature representation of a citation entry, a brief network model for inter-object similarity computation, and a special cluster evaluation technique are discussed in detail.
14797393
A system for analyzing and indexing human-motion databases
We demonstrate a data-driven approach for representing, compressing, and indexing human-motion databases. Our modeling approach is based on piecewise-linear components that are determined via a divisive clustering method. Selection of the appropriate linear model is determined automatically via a classifier using a subspace of the most significant, or principle features (markers). We show that, after offline training, our model can accurately estimate and classify human motions. We can also construct indexing structures for motion sequences according to their transition trajectories through these linear components. Our method not only provides indices for whole and/or partial motion sequences, but also serves as a compressed representation for the entire motion database. Our method also tends to be immune to temporal variations, and thus avoids the expense of time-warping.
13632117
A Novel Intrusion Detection Method Based on Principle Component Analysis in Computer Security
Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer security in recent years. In this paper, a new intrusion detection method based on Principle Component Analysis (PCA) with low overhead and high efficiency is presented. System call data and command sequences data are used as information sources to validate the proposed method. The frequencies of individual system calls in a trace and individual commands in a data block are computed and then data column vectors which represent the traces and blocks of the data are formed as data input. PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for real-time intrusion detection.
19006089
Processing of massive audit data streams for real-time anomaly intrusion detection
Intrusion detection is an important technique in the defense-in-depth network security framework. Most current intrusion detection models lack the ability to process massive audit data streams for real-time anomaly detection. In this paper, we present an effective anomaly intrusion detection model based on Principal Component Analysis (PCA). The model is more suitable for high speed processing of massive data streams in real-time from various data sources by considering the frequency property of audit events than by use of the transition property or the correlation property. It can serve as a general framework that a practical Intrusion Detection Systems (IDS) can be implemented in various computing environments. In this method, a multi-pronged anomaly detection model is used to monitor various computer system and network behaviors. Three sources of data, system call data from the University of New Mexico (lpr) and from KLINNS Lab of Xi'an Jiaotong University (ftp), shell command data from AT&T Research laboratory, and network data from MIT Lincoln Lab, are used to validate the model and the method. The frequencies of individual system calls generated by one process and of individual commands embedded in one command block as well as features extracted in one network connection are transformed into an input data vector. Our method is employed to reduce the high dimensional data vectors and thus the detection is handled in a lower dimension with high efficiency and low use of system resources. The distance between a vector and its reconstruction in the reduced subspace is used for anomaly detection. Empirical results show that our model is promising in terms of detection accuracy and computational efficiency, and thus amenable for real-time intrusion detection.