Efficient Query Processing with Compiled Knowledge Bases

In an Object Relational Data Warehousing (ORDW) environment, the semantics of data and queries can be explicitly captured, represented, and utilized based on is-a and class composition hierarchies, thereby resulting in more efficient OLAP query processing. In this chapter, we show the efficacy in building semantic-rich hybrid data indexes incorporating Structural Join Index Hierarchy (SJIH) on the ORDW views. Given a set of queries, we use a hill-climbing heuristic algorithm to select (near) optimal SJIHs, thereby embedding query semantics into the indexing framework. Finally, by a cost model, we analyze the effectiveness of our approach vis-a-vis the pointer chasing approach.

Supporting Efficient Query Processing on COB+ -Tree

International Conference on Mechanical Engineering and Technology (ICMET-London 2011) ◽

10.1115/1.859896.paper151 ◽

2011 ◽

pp. 741-745

Keyword(s):

Query Processing ◽

Efficient Query Processing

Efficient Query Processing with Mutual Privacy Protection for Location-Based Services

Database Systems for Advanced Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-32049-6_19 ◽

2016 ◽

pp. 299-313 ◽

Cited By ~ 8

Author(s):

Shushu Liu ◽

An Liu ◽

Lei Zhao ◽

Guanfeng Liu ◽

Zhixu Li ◽

...

Keyword(s):

Query Processing ◽

Privacy Protection ◽

Location Based Services ◽

Efficient Query Processing

Effective keyword search on graph data using limited root redundancy of answer trees

International Journal of Web Information Systems ◽

10.1108/ijwis-10-2017-0070 ◽

2018 ◽

Vol 14 (3) ◽

pp. 299-316 ◽

Cited By ~ 2

Author(s):

Chang-Sup Park

Keyword(s):

Query Processing ◽

Keyword Search ◽

Information Need ◽

Keyword Query ◽

Content Type ◽

Graph Data ◽

Distinct Root ◽

Path Index ◽

Complex Graph ◽

Efficient Query Processing

Purpose This paper aims to propose a new keyword search method on graph data to improve the relevance of search results and reduce duplication of content nodes in the answer trees obtained by previous approaches based on distinct root semantics. The previous approaches are restricted to find answer trees having different root nodes and thus often generate a result consisting of answer trees with low relevance to the query or duplicate content nodes. The method allows limited redundancy in the root nodes of top-k answer trees to produce more effective query results. Design/methodology/approach A measure for redundancy in a set of answer trees regarding their root nodes is defined, and according to the metric, a set of answer trees with limited root redundancy is proposed for the result of a keyword query on graph data. For efficient query processing, an index on the useful paths in the graph using inverted lists and a hash map is suggested. Then, based on the path index, a top-k query processing algorithm is presented to find most relevant and diverse answer trees given a maximum amount of root redundancy allowed for a set of answer trees. Findings The results of experiments using real graph datasets show that the proposed approach can produce effective query answers which are more diverse in the content nodes and more relevant to the query than the previous approach based on distinct root semantics. Originality/value This paper first takes redundancy in the root nodes of answer trees into account to improve the relevance and content nodes redundancy of query results over the previous distinct root semantics. It can satisfy the users’ various information need on a large and complex graph data using a keyword-based query.