9181955
Answering Queries with Database Restrictions
I am a Ph.D. student in the Computer Science Department at Stanford University. My main research interests include query reformulation in the database domain. In particular, I work on the problems of how to do query planning and reformulation when relations have limited access patterns [7,9]. That is, the binding patterns of the relations require values to be specified for certain attributes in order to retrieve data from a relation. Most of my work is based on the TSIM-MIS project (http://www-db.stanford.edu/tsimmis/), a data-integration project [2,10,11] at Stanford.
2113121
Computing capabilities of mediators
Existing data-integration systems based on the mediation architecture employ a variety of mechanisms to describe the query-processing capabilities of sources. However, these systems do not compute the capabilities of the mediators based on the capabilities of the sources they integrate. In this paper, we proposed a framework to capture a rich variety of query-processing capabilities of data sources and mediators. We present algorithms to compute the set of supported queries of a mediator, based on the capability limitations of its sources. Our algorithms take into consideration a variety of query-processing techniques employed by mediators to enhance the set of supported queries.
1386080
Using Constraints to Describe Source Contents in Data Integration Systems
In data integration systems, information sources often have constraints, such as "all houses stored at a source have a unique address." These constraints are useful in computing answers to queries and facilitating information exchange.
10902397
Protecting Individual Information Against Inference Attacks in Data Publishing
In many data-publishing applications, the data owner needs to protect sensitive information pertaining to individuals. Meanwhile, certain information is required to be published. The sensitive information could be considered as leaked, if an adversary can infer the real value of a sensitive entry with a high confidence. In this paper we study how to protect sensitive data when an adversary can do inference attacks using association rules derived from the data. We formulate the inference attack model, and develop complexity results on computing a safe partial table. We classify the general problem into subcases based on the requirements of publishing information, and propose the corresponding algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data.
8510271
Supporting Efficient Record Linkage for Large Data Sets Using Mapping Techniques
This paper describes an efficient approach to record linkage. Given two lists of records, the record-linkage problem consists of determining all pairs that are similar to each other, where the overall similarity between two records is defined based on domain-specific similarities over individual attributes. The record-linkage problem arises naturally in the context of data cleansing that usually precedes data analysis and mining. Since the scalability issue of record linkage was addressed in [21], the repertoire of database techniques dealing with multidimensional data sets has significantly increased. Specifically, many effective and efficient approaches for distance-preserving transforms and similarity joins have been developed. Based on these advances, we explore a novel approach to record linkage. For each attribute of records, we first map values to a multidimensional Euclidean space that preserves domain-specific similarity. Many mapping algorithms can be applied, and we use the Fastmap approach [16] as an example. Given the merging rule that defines when two records are similar based on their attribute-level similarities, a set of attributes are chosen along which the merge will proceed. A multidimensional similarity join over the chosen attributes is used to find similar pairs of records. Our extensive experiments using real data sets show that our solution has very good efficiency and recall.
7967297
2D BubbleUp: Managing Parallel Disks for Media Servers
In this study we present a scheme called two-dimensional BubbleUp (2DB) for managing parallel disks in a multimedia server. Its goal is to reduce initial latency for interactive multimedia applications, while balancing disk loads to maintain high throughput. The 2DB scheme consists of a data placement and a request scheduling policy. The data placement policy replicates frequently accessed data and places them cyclically throughout the disks. The request scheduling policy attempts to maintain free ``service slots'''' in the immediate future. These slots can then be used to quickly service newly arrived requests. Through examples and simulation, we show that our scheme significantly reduces initial latency and maintains throughput comparable to that of the traditional schemes.
18473631
Cryptanalysis of a Chaotic Neural Network Based Multimedia Encryption Scheme
Recently, Yen and Guo proposed a chaotic neural network (CNN) for signal encryption, which was suggested as a solution for protection of digital images and videos. The present paper evaluates the security of this CNN-based encryption scheme, and points out that it is not secure from the cryptographical point of view: 1) it can be easily broken by known/chosen-plaintext attacks; 2) its security against the brute-force attack was much over-estimated. Some experiments are shown to support the results given in this paper. It is also discussed how to improve the encryption scheme.
39036081
Tongue image matching using color content
This paper addresses the problem of color image matching in medical diagnosis. The color matching of tongue images in different color spaces with different metrics have been investigated and is reported in this article. Two new metrics namely, sorted metric and probabilistic combined metric, are proposed. Existing distance measurements in coordinate space do not satisfy the reflexivity axiom. That means, they are not the valid metrics. To overcome this limitation, the sorted metric in coordinate space is proposed in coordinate space. To improve the matching performance, a probabilistic combined metric is proposed based on the theory of combining classifier. These metrics are applied for the matching of tongue color images and the results are encouraging.
17566608
Porosity of mutually nearest and mutually furthest points in Banach spaces
Let X be a real strictly convex and Kadec Banach space and G a nonempty closed relatively boundedly weakly compact subset of X. Let B(X) (resp. K(X)) be the family of nonempty bounded closed (resp. compact) subsets of X endowed with the Hausdorff distance and let BG(X) denote the closure of the set {A ∈ B(X) : A ∩ G = φ} and KG(X) = BG(X)∩K(X). We introduce the admissible family A of B(X) and prove that EAo(G) (resp. EoA(G)), the set of all subsets F ∈ A ⊆ BG(X) (resp. F ∈ A ⊆ BG(X)) such that the minimization problem min(F, G) (resp. the maximization problem max(F, G)) is well-posed, is a dense Gδ-subset of A. Furthermore, when X is uniformly convex, we prove that A\EAo(G) and A\EoA(G) are σ-porous in A.
17199559
Data Mining for Direct Marketing: Problems and Solutions
Direct marketing is a process of identifying likely buyers of certain products and promoting the products accordingly. It is increasingly used by banks, insurance companies, and the retail industry. Data mining can provide an effective tool for direct marketing. During data mining, several specific problems arise. For example, the class distribution is extremely unbalanced (the response rate is about 1%), the predictive accuracy is no longer suitable for evaluating learning methods, and the number of examples can be too large. In this paper, we discuss methods of coping with these problems based on our experience on direct-marketing projects using data mining.
3427735
RankSQL: Supporting Ranking Queries in Relational Database Management Systems
Ranking queries (or top-k queries) are dominant in many emerging applications, e.g., similarity queries in multimedia databases, searching Web databases, middleware, and data mining. The increasing importance of top-k queries warrants an efficient support of ranking in the relational database management system (RDBMS) and has recently gained the attention of the research community. Top-k queries aim at providing only the top k query results, according to a user-specified ranking function, which in many cases is an aggregate of multiple criteria. The following is an example top-k query.