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.
1269505
Multi-dimensional search for personal information management systems
With the explosion in the amount of semi-structured data users access and store in personal information management systems, there is a need for complex search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools usually index text content, allowing for some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multi-dimensional approach to semi-structured data searches in personal information management systems by allowing users to provide fuzzy structure and metadata conditions in addition to keyword conditions. Our techniques provide a complex query interface that is more comprehensive than content-only searches as it considers three query dimensions (content, structure, metadata) in the search. We propose techniques to individually score each dimension, as well as a framework to integrate the three dimension scores into a meaningful unified score. Our work is integrated in Wayfinder, an existing fully-functioning file system. We perform a thorough experimental evaluation of our techniques to show the effect of approximating individual dimensions on the overall scores and ranks of files, as well as on query performance. Our experiments show that our scoring strategy adequately takes into account the approximation in each dimension to efficiently evaluate fuzzy multi-dimensional queries. In addition, fuzzy query conditions in non-content dimensions can significantly improve scoring (and thus ranking) accuracy.
8292023
Fuzzy Multi-Dimensional Search in the Wayfinder File System
With the explosion in the amount of semi-structured data users access and store, there is a need for complex search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools usually index text content, allowing for some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multidimensional querying approach to semi-structured data searches in personal information systems by allowing users to provide fuzzy structure and metadata conditions in addition to traditional keyword conditions. The provided query interface is more comprehensive than content-only searches as it considers three query dimensions (content, structure, metadata) in the search. We have implemented our proposed approach in the Wayfinder file system. In this demo, we will use this implementation to both present an overview of the unified scoring framework underlying the fuzzy multi-dimensional querying approach and demonstrate its potential in improving search 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.
31496165
Elliptic curves cryptosystem implementation based on a look-up table sharing scheme
Projective point elliptic curve cryptography (ECC) involves many look-up table-based field multiplications. In this paper, a novel lookup table sharing scheme is proposed to significantly reduce the number of the field multiplications and number of lookup tables. By requiring much less lookup table generation time, the proposed system offers high-speed performance for hardware realizations. Its application in a reconfigurable PC-FPGA platform is investigated, which can efficiently handle requests for different ECC key length and curves and is useful for many computer network applications
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.
13485709
Three-dimensional conceptual model for service-oriented simulation
In this letter, we propose a novel three-dimensional conceptual model for an emerging service-oriented simulation paradigm. The model can be used as a guideline or an analytic means to find the potential and possible future directions of the current simulation frameworks. In particular, the model inspects the crossover between the disciplines of modeling and simulation, service-orientation, and software/systems engineering. Finally, two specific simulation frameworks are studied as examples.
33260694
Consistent Answers from Integrated XML Data
We consider the problem of using query transformation to compute consistent answers when queries are posed to virtual XML data integration systems, which are specified following the local-as-view approach. This is achieved in two steps. First the given query is transformed to a new query with global constraints considered, then the new query is rewritten to queries on the underlying data sources by reversing rules in view definitions. The XPath query on the global system can be transformed in XQuery. We implement prototypes of our method, and evaluate our framework and algorithms in the experiment.