3430787
iOmS: An Agent-Based P2P Framework for Ubiquitous Workflow
Workflow is referred to automatic business process, and current involving methods mainly depend on traditional centralized C/S infrastructure and email systems. As far as we know, the existing methods have following weaknesses in nature: 1) The abilities of workflow processing is difficult to scale up; 2) Workflow execution is not flexible; 3) Workflow transfer process is not reliable; 4) Handheld devices in mobile environments are seldom considered. However, peer-to-peer technology is characterized by decentralized control, large scale, and extreme dynamism, while agent technology can construct overlay network more easily and process workflow automatically. In this paper, we present a novel framework based on office-secretary model, which utilizes concept of peer-to-peer, agent and transaction mechanism. In particular, we believe that iOmS framework could easily support scalable, dynamic, reliable, and ubiquitous workflow applications.
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.
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.
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.
1744203
SPARK: A Keyword Search Engine on Relational Databases
Relational database is the most widely adopted and mature technology for information storage. As many services on the Web (e.g., blog and wiki sites) and advanced applications (e.g., customer relationship management systems and content management systems) are built on RDBMSs, increasing amount of text data is now stored in relational databases, accompanied by increasing demands of retrieving relevant information by free-style keyword search.
3251955
Containment join size estimation: models and methods
Recent years witnessed an increasing interest in researches in XML, partly due to the fact that XML has now become the de facto standard for data interchange over the internet. A large amount of work has been reported on XML storage models and query processing techniques. However, few works have addressed issues of XML query optimization. In this paper, we report our study on one of the challenges in XML query optimization: containment join size estimation. Containment join is well accepted as an important operation in XML query processing. Estimating the size of its results is no doubt essential to generate efficient XML query processing plans. We propose two models, the interval model and the position model, and a set of estimation methods based on these two models. Comprehensive performance studies were conducted. The results not only demonstrate the advantages of our new algorithms over existing algorithms, but also provide valuable insights into the tradeoff among various parameters.
19598296
Type I HARQ performance modeling and evaluation of mobile WiMAX for network simulators
While the development of Mobile WiMAX devices is still an ongoing process, complete and accurate simulations become more important in order to study the performance of Mobile WiMAX based broadband wireless access networks. To further improve network simulation models for Mobile WiMAX, we have theoretically modeled the Hybrid Automatic Retransmission reQuest (HARQ) mechanism and evaluated its performance and accuracy. In this paper, we present the design and implementation methodology of the Mobile WiMAX HARQ simulation model in system-level network simulators in order to gain the benefit of link quality improved by enabling HARQ. Our results show that HARQ achieves higher throughput even at low signal strength which can be considered as non-line-of-sight (NLOS) environments. We believe the implementation of HARQ in the network simulation model and its evaluation studies will provide profound understanding to Mobile WiMAX field test studies.
2285434
Towards multidimensional subspace skyline analysis
The skyline operator is important for multicriteria decision-making applications. Although many recent studies developed efficient methods to compute skyline objects in a given space, none of them considers skylines in multiple subspaces simultaneously. More importantly, the fundamental problem on the semantics of skylines remains open: Why and in which subspaces is (or is not) an object in the skyline? Practically, users may also be interested in the skylines in any subspaces. Then, what is the relationship between the skylines in the subspaces and those in the super-spaces? How can we effectively analyze the subspace skylines? Can we efficiently compute skylines in various subspaces and answer various analytical queries?In this article, we tackle the problem of multidimensional subspace skyline computation and analysis. We explore skylines in subspaces. First, we propose the concept of Skycube, which consists of skylines of all possible nonempty subspaces of a given full space. Once a Skycube is materialized, any subspace skyline queries can be answered online. However, Skycube cannot fully address the semantic concerns and may contain redundant information. To tackle the problem, we introduce a novel notion of skyline group which essentially is a group of objects that coincide in the skylines of some subspaces. We identify the decisive subspaces that qualify skyline groups in the subspace skylines. The new notions concisely capture the semantics and the structures of skylines in various subspaces. Multidimensional roll-up and drill-down analysis is introduced. We also develop efficient algorithms to compute Skycube, skyline groups and their decisive subspaces. A systematic performance study using both real data sets and synthetic data sets is reported to evaluate our approach.
6124530
Discovering High-Order Periodic Patterns
Abstract.Discovery of periodic patterns in time series data has become an active research area with many applications. These patterns can be hierarchical in nature, where a higher-level pattern may consist of repetitions of lower-level patterns. Unfortunately, the presence of noise may prevent these higher-level patterns from being recognized in the sense that two portions (of a data sequence) that support the same (high-level) pattern may have different layouts of occurrences of basic symbols. There may not exist any common representation in terms of raw symbol combinations; and hence such (high-level) pattern may not be expressed by any previous model (defined on raw symbols or symbol combinations) and would not be properly recognized by any existing method. In this paper, we propose a novel model, namely meta-pattern, to capture these high-level patterns. As a more flexible model, the number of potential meta-patterns could be very large. A substantial difficulty lies in how to identify the proper pattern candidates. However, the well-known Apriori property is not able to provide sufficient pruning power. A new property, namely component location property, is identified and used to conduct the candidate generation so that an efficient computation-based mining algorithm can be developed. Last, but not least, we apply our algorithm to some real and synthetic sequences and some interesting patterns are discovered.