10270539
Efficient Optimization of Multiple Subspace Skyline Queries
We present the first efficient sound and complete algorithm (i.e., AOMSSQ) for optimizing multiple subspace skyline queries simultaneously in this paper. We first identify three performance problems of the naíve approach (i.e., SUBSKY) which can be used in processing arbitrary single-subspace skyline query. Then we propose a cell-dominance computation algorithm (i.e., CDCA) to efficiently overcome the drawbacks of SUBSKY. Specially, a novel pruning technique is used in CDCA to dramatically decrease the query time. Finally, based on the CDCA algorithm and the share mechanism between subspaces, we present and discuss the AOMSSQ algorithm and prove it sound and complete. We also present detailed theoretical analyses and extensive experiments that demonstrate our algorithms are both efficient and effective.
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.
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.
3416923
Efficient Computation of the Skyline Cube
Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper. we consider the problem of efficiently computing a SKYCUBE, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such "shared-nothing" algorithms are inefficient. We develop several computation sharing strategies based on effectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute SKYCUBE efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is shown that new algorithms significantly outperform the naive ones.
18851667
Sensor Density for Complete Information Coverage in Wireless Sensor Networks
Coverage is a very important issue in wireless sensor networks. Current literature defines a point to be covered if it is within the sensing radius of at least one sensor. This is a conservative definition of coverage and we have previously proposed a new notion of information coverage. Compared with the conventional definition of coverage, a point can still be information covered even if it is not within the sensing disk of any sensor. The density requirements for complete information coverage of a field are analyzed and simulated for a random sensor deployment. Our results show that significant savings in terms of sensor density can be achieved with information coverage.
14028781
Study of an energy efficient multi rate scheme for wireless sensor network MAC protocol
Real-time Digital Signal Processing (DSP) applications in wireless sensor network are often multi-rate in nature. These multi rate attributes provide additional significant opportunities in network design for achieving higher energy efficiency, besides traditional approaches of saving energy. In this paper, we propose a novel multi rate sensor network scheme to take advantage of power scaling for saving energy consumption. It can be easily synthesized into existing wireless sensor network MAC protocols, providing multi rate service for upper layer, and achieving communication energy efficiency. By monitoring a desirable BER threshold as the critical system parameter, the quality of received multi-rate data is assured. Based on this BER and the corresponding modulation scheme, the SNR at receiver end can be derived. The transmission power can be supplied optimally according to this SNR and the channel attenuation between sender and receiver. This dynamic power scaling scheme can lead to optimal energy efficiency since it demands that only a necessary amount of communication energy be consumed for multi-rate DSP applications. Our simulation shows that this multi data rate scheme with dynamic power scaling achieves energy saving significantly
6042260
Mining protein family specific residue packing patterns from protein structure graphs
Finding recurring residue packing patterns, or spatial motifs, that characterize protein structural families is an important problem in bioinformatics. We apply a novel frequent subgraph mining algorithm to three graph representations of protein three-dimensional (3D) structure. In each protein graph, a vertex represents an amino acid. Vertex-residues are connected by edges using three approaches: first, based on simple distance threshold between contact residues; second using the Delaunay tessellation from computational geometry, and third using the recently developed almost-Delaunay tessellation approach.Applying a frequent subgraph mining algorithm to a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, we typically identify several hundred common subgraphs equivalent to common packing motifs found in the majority of proteins in the family. We also use the counts of motifs extracted from proteins in two different SCOP families as input variables in a binary classification experiment. The resulting models are capable of predicting the protein family association with the accuracy exceeding 90 percent. Our results indicate that graphs based on both almost-Delaunay and Delaunay tessellations are sparser than the contact distance graphs; yet they are robust and efficient for mining protein spatial motif.
6867733
Threshold-based probabilistic top-k dominating queries
Recently, due to intrinsic characteristics in many underlying data sets, a number of probabilistic queries on uncertain data have been investigated. Top-k dominating queries are very important in many applications including decision making in a multidimensional space. In this paper, we study the problem of efficiently computing top-k dominating queries on uncertain data. We first formally define the problem. Then, we develop an efficient, threshold-based algorithm to compute the exact solution. To overcome some inherent computational deficiency in an exact computation, we develop an efficient randomized algorithm with an accuracy guarantee. Our extensive experiments demonstrate that both algorithms are quite efficient, while the randomized algorithm is quite scalable against data set sizes, object areas, k values, etc. The randomized algorithm is also highly accurate in practice.