10551742
Modeling semantics and structure of discussion threads
The abundant knowledge in web communities has motivated the research interests in discussion threads. The dynamic nature of discussion threads poses interesting and challenging problems for computer scientists. Although techniques such as semantic models or structural models have been shown to be useful in a number of areas, they are inefficient in understanding discussion threads due to the temporal dependence among posts in a discussion thread. Such dependence causes that semantics and structure coupled with each other in discussion threads. In this paper, we propose a sparse coding-based model named SMSS to Simultaneously Model Semantic and Structure of discussion threads.
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.
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.
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.
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.
5733267
KemaDom: a web server for domain prediction using kernel machine with local context
Predicting domains of proteins is an important and challenging problem in computational biology because of its significant role in understanding the complexity of proteomes. Although many template-based prediction servers have been developed, ab initio methods should be designed and further improved to be the complementarity of the template-based methods. In this paper, we present a novel domain prediction system KemaDom by ensembling three kernel machines with the local context information among neighboring amino acids. KemaDom, an alternative ab initio predictor, can achieve high performance in predicting the number of domains in proteins. It is freely accessible at and .
2503641
Research on Simulation Service Ontology and Its Matchmaking Model
Web Service is the key supporting technology for large scale resource sharing and application integration. Service-Oriented distributed simulation is becoming a very hot research topic in M&S. Aiming at the problem of simulation model service description and discovery, semantics-oriented simulation service ontology and description models are presented in this paper. A matchmaking model named ldquofour types and three levelsrdquo is proposed for simulation services discovery. Similarity functions are introduced to rank and select the simulation services. In particularly, some key matching algorithms are studied.
10163897
Accelerating approximate subsequence search on large protein sequence databases
In this paper, we study the problem on how to build a persistent index structure for protein sequences to support approximate match. The suffix tree has been proposed as a solution to index sequence database and has been deployed on organizing DNA sequences (Hunt et al. (2001)). Unfortunately, it suffers from the problem of "memory bottleneck" that prevents it from being applied efficiently to a large database. The performance even degrades further for protein database due to a larger fanout at each node. Here, we employ an indexing structure, called BASS-tree, to support approximate match in sublinear time on a large protein database. We call this indexing method the sequence approximate match index method. The search of approximate matches can be properly directed to the portion in the database with a high potential of matching quickly. It is demonstrated in our experiments that the potential performance improvement is in an order of magnitude over alternative methods such as the BLAST algorithm and the suffix tree.
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.
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.