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ISPRS Int. J. Geo-Inf. 2016, 5(5), 54; doi:10.3390/ijgi5050054

Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery

1
NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
2
NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 1 February 2016 / Revised: 21 March 2016 / Accepted: 18 April 2016 / Published: 25 April 2016
(This article belongs to the Special Issue Geographic Information Retrieval)
View Full-Text   |   Download PDF [3076 KB, uploaded 25 April 2016]   |  

Abstract

Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upon in this paper as a critical step for extracting usage patterns. However, reconstructing user sessions from raw web logs has always been difficult, as a session identifier tends to be missing in most data portals. To address this problem, we propose two session identification methods, including time-clustering-based and time-referrer-based methods. We also present the workflow of session reconstruction and discuss the approach of selecting appropriate thresholds for relevant steps in the workflow. The proposed session identification methods and workflow are proven to be able to extract data access patterns for further pattern analyses of user behavior and improvement of data discovery for more relevancy data ranking, suggestion, and navigation. View Full-Text
Keywords: web usage mining; session identification and reconstruction; crawler detection; semantic search; data discovery web usage mining; session identification and reconstruction; crawler detection; semantic search; data discovery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jiang, Y.; Li, Y.; Yang, C.; Armstrong, E.M.; Huang, T.; Moroni, D. Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery. ISPRS Int. J. Geo-Inf. 2016, 5, 54.

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ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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