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.
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.
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.
33982421
Positive Sample Enhanced Angle-Diversity Active Learning for SVM Based Image Retrieval
Active learning is a promising tool to improve the performance of content-based image retrieval (CBIR). As a commonly used active learning approach, angle-diversity provides the most informative images to user for feedback. However, it suffers from the problem that the query concept is diverse and the numbers of the positive and the negative images are imbalanced. As a consequence, the positive samples obtained by active learning are inadequate, which degrades the learning efficiency. To deal with this issue, we propose a novel method based on angle-diversity and hyperplane shifting to increase the number of positive images in the active learning results. The experiment is conducted on a test data set with 10,000 images. Compared with the traditional angle-diversity technique, our method can improve the retrieval performance significantly.
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.
34121335
Data-Aware Clustering Hierarchy for Wireless Sensor Networks
In recent years, the wireless sensor network (WSN) is employed a wide range of applications. But existing communication protocols for WSN ignore the characteristics of collected data and set routes only according to the mutual distance and residual energy of sensors. In this paper we propose a Data-Aware Clustering Hierarchy (DACH), which organizes the sensors based on both distance information and data distribution in the network Furthermore, we also present a multi-granularity query processing method based on DACH, which can estimate the query result more efficiently. Our empirical study shows that DACH has higher energy efficiency than Low-Energy Adaptive Clustering Hierarchy (LEACH), and the multi-granularity query processing method based on DACH brings more accurate results than a random access system using same cost of energy.
15012785
An area-reduced scheme for modulo 2/sup n/-1 addition/subtraction
In this paper, we present a versatile area-reduced scheme for modulo 2/sup n/-1 adders and subtracters using a MUX-based increment/decrement algorithm. A FPGA-based comparison of the proposed modulo adder and the conventional modulo adder designs is carried out. The implementation results show that the proposed adder reduces the area close to 30% compared with the modulo adder of Bayoumi et al. The delay and the power are also reduced around 10%. In addition, it is also shown that the proposed design requires less hardware resources than the parallel-prefix modulo adder of Kalampoukas et al. while providing a comparable operation speed.
17409738
Flexible Querying of Personal Information
The amount of personal data that users store and access in personal information systems has grown massively. Simple flexible search tools are a necessity for users to be able to access the data they need. In addition, the file organization that is typical in file systems is not a practical logical view anymore as applications may store information across files, or combine different objects into a single file. Users cannot be expected to know or remember the exact location and specific details about the data they are looking for; search techniques should allow for some approximation during query processing in order to return useful results while accounting for possible errors in the query. We present a scoring framework that takes into account approximation in both the content and structural dimensions of the data. Our query model extends the search to consider information across file boundaries. We implemented a prototype of our search techniques over a real data set and report on our experimental results.
9278394
Keyword search on structured and semi-structured data
Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.
15503361
AB-Index: An Efficient Adaptive Index for Branching XML Queries
Query-adaptive XML indexing has been proposed and shown to be an efficient way to accelerate XML query processing, because it dynamically adapts to the workload. However, existing adaptive index lack of support for branching queries, and also with low efficiency for query processing and adaptation operations. In this paper, we propose a new Adaptive index for Branching queries, which is named as AB-Index. It is designed to support XML path queries with branching predicates. Efficient index construction, query processing, and index adaptation algorithms are proposed for AB-Index. In the experiments, the proposed index is demonstrated to outperform the state-of-the-art approach in the area of adaptive index in terms of query and adaptation efficiencies.
15381526
A general framework for adaptive and online detection of web attacks
Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we propose a novel general framework for adaptive and online detection of web attacks. The general framework can be based on any online clustering methods. A detection model based on the framework is able to learn online and deal with "concept drift" in web audit data streams. Str-DBSCAN that we extended DBSCAN to streaming data as well as StrAP are both used to validate the framework. The detection model based on the framework automatically labels the web audit data and adapts to normal behavior changes while identifies attacks through dynamical clustering of the streaming data. A very large size of real HTTP Log data collected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness.