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.
47178135
Efficient similarity joins for near duplicate detection
With the increasing amount of data and the need to integrate data from multiple data sources, a challenging issue is to find near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. Experimental results show that our proposed algorithms can achieve up to 2.6x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem.
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.
20747285
A transformation model from DEVS to SMP2 based on MDA
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.
1232737
Extract Frequent Pattern from Simple Graph Data
Mining the frequent pattern from data set is one of the key success stories of data mining research. Currently, most of the efforts are focused on the independent data such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to gain the frequent pattern from these relations is the objective in this paper. We use graphs to model the relations, and select a simple type for analysis. Combining the graph-theory and algorithms to generate frequent patterns, a new structure SFP-Tree and an algorithm, which can mine these simple graphs efficiently, have been proposed. We evaluate the performance of the algorithm by experiments with synthetic datasets. The empirical results show that the SFP can do the job well. At the end of this paper, the potential improvement of SFP is mentioned.