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.
9434684
iMecho: an associative memory based desktop search system
Traditional desktop search engines only support keyword based search that needs exact keyword matching to find resources. However, users generally have a vague picture of what is stored but forget the exact location and keywords of the resource. According to observations of human associative memory, people tend to remember things from some memory fragments in their brains and these memory fragments are connected by memory cues of user activity context. We developed iMecho (My Memory Echo), an associative memory based desktop search system, which exploits such associations and contexts to enhance traditional desktop search. Desktop resources are connected with semantic links mined from explicit and implicit user activities according to specific access patterns. Using these semantic links, associations among memory fragments can be built or rebuilt in a user's brain during a search. Moreover, our personalized ranking scheme uses these links together with a user's personal preferences to rank results by both relevance and importance to the user. In addition, the system provides a faceted search feature and association graph navigation to help users refine and associate search results generated by full-text keyword search. Our experiments investigating precision and recall quality of iMecho prototype show that the association-based search system is superior to the traditional keyword search in personal search engines since it is closer to the way that human associative memory works.
18683414
Extending Tree Automata to Obtain Consistent Query Answer from Inconsistent XML Document
Data may contain inconsistencies that violate integrity constraints, the consistent query answering problem attempts to find answers common for every possible repair. In this paper, we study how to handle the inconsistent XML document, which conforms to the DTD, while violates constraints. We consider three types of constraints, including functional dependencies, keys and foreign keys. 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 labelled XML document, which is an extension to the traditional tree model of XML with a function to indicate the unreliable nodes. We then give the definition of minimal labelled XML document, a representation of all possible repairs and can be used for consistent query answering. We provide a method for building an extended bottom-up tree automaton. The automaton can, in only one pass, not only check the validity of an XML document w.r.t DTD and integrity constraints, but also generate the corresponding minimal labelled XML document
3247167
Storage and Query over Encrypted Character and Numerical Data in Database
There is some private and sensitive data in database, which need to be protected from attacking. In order to reinforce the security of data, an effective mechanism, cryptographic support has been widely used. However, we must make a tradeoff between the performance and the security because encryption and decryption greatly degrade the query performance. To solve such a problem, a novel approach is proposed in this paper that can quickly execute SQL query on the encrypted data. For character data, it not only encrypts them, but also turns the character data into characteristic values via a characteristic function and stores them as additional fields. For numerical data, it not only encrypts them, but also creates its B+ tree index before the encryption in order to keep the ordering of each record in the index. Furthermore, we give the algorithms of querying the encrypted data based on the storage models. Results of sets of experiments validate the functionality and usability of our approach
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.
28002142
Consistent data for inconsistent XML document
XML document may contain inconsistencies that violate predefined integrity constraints, which causes the data inconsistency problem. In this paper, we consider how to get the consistent data from an inconsistent XML document. 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. First we give a general constraint model for XML, which can express the commonly discussed integrity constraints, including 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 node value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase method to generate repairs, and prove some important properties of the chase. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data. We also implement prototypes of our method, and evaluate our framework and algorithms in the experiment.
7938529
Efficient mining of frequent subgraphs in the presence of isomorphism
Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees, which have been studied extensively. We propose a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework we have developed to reduce the number of redundant candidates proposed. Our empirical study on synthetic and real datasets demonstrates that FFSM achieves a substantial performance gain over the current start-of-the-art subgraph mining algorithm gSpan.
2987996
Mining patterns in long sequential data with noise
Pattern discovery in time series data has been a problem of great importance in many fields, e.g., computational biology, performance analysis, consumer behavior, etc. Recently, considerable amount of research has been carried out in this area. The facts that the input data is typically very large and noises may present in various formats pose great challenge to the mining process. Recently, we have made several new research advances in this area. In this paper, we present some of them. We will survey new models proposed to address different types of noises as well as scalable algorithms developed for efficiently mining patterns under each model.
2396831
Mining coherent patterns from heterogeneous microarray data
Microarray technology is a powerful tool for geneticists to monitor interactions among tens of thousands of genes simultaneously. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. However, these methods assume that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data. We compare our model with previously proposed models. We analyze the search space of the problem and give a naïve solution for it.
14374205
Understanding Social Welfare Service Patterns Using Sequential Analysis
Classical exploratory data analysis methods in statistics and many of the earlier KDD methods tend to focus only on basic data types, (interval or categorical data) as the unit of analysis. However, some information cannot be interpreted unless the data is treated as a unit. For example, viewing the data as sequences of sets can reveal useful information that could not be extracted in any other way. Analyzing suitable databases from such a perspective can assist many social scientists in their work.
14078437
CLUSEQ: efficient and effective sequence clustering
Analyzing sequence data has become increasingly important recently in the area of biological sequences, text documents, Web access logs, etc. We investigate the problem of clustering sequences based on their sequential features. As a widely recognized technique, clustering has proven to be very useful in detecting unknown object categories and revealing hidden correlations among objects. One difficulty that prevents clustering from being performed extensively on sequence data (in categorical domain) is the lack of an effective yet efficient similarity measure. Therefore, we propose a novel model (CLUSEQ) for sequence cluster by exploring significant statistical properties possessed by the sequences. The conditional probability distribution (CPD) of the next symbol given a preceding segment is derived and used to characterize sequence behavior and to support the similarity measure. A variation of the suffix tree, namely probabilistic suffix tree, is employed to organize (the significant portion of) the CPD in a concise way. A novel algorithm is devised to efficiently discover clusters with high quality and is able to automatically adjust the number of clusters to its optimal range via a unique combination of successive new cluster generation and cluster consolidation. The performance of CLUSEQ has been demonstrated via extensive experiments on several real and synthetic sequence databases.