Patent application EP17154919A (EP), filed 2017-02-07
Publication number: EP3226156A1
Title: A SYSTEM AND A METHOD TO PROVIDE VISITOR INFORMATION ABOUT VISITOR REQUESTS TO A DATASET OF LINKED RDF DATA
Abstract: A system to provide visitor information about visitor requests to a dataset of linked RDF data, the system comprising: a loading module with a log ingestion unit to read and parse access logs of visitor requests from the dataset; a linked data-specific metric extractor to extract metrics related specifically to linked data, the metrics providing visitor information related to individual RDF resources in the dataset; a request counter in the linked data-specific metric extractor to count requests issued by visitors to each resource in the dataset, the request counter including requests using RDF dereferencing and requests using SPARQL queries, a SPARQL query count including separation of the RDF resources queried into URIs representing RDF instances, RDF classes, RDF properties and RDF graphs; a database loader to store the visitor information computed by the linked data- specific metric extractor in a database which is linked to a query unit to answer enquiries about the visitor information.
IPC codes: G06F  17/    30            A I
G06F  16/    25            A I
Claims: number: 1
text: A system to provide visitor information about visitor requests to a dataset of linked RDF data, the system comprising:
number: 2
text: A system according to claim 1, wherein the linked data-specific metric extractor includes a SPARQL metric extractor to extract metrics related to SPARQL query types and/or SPARQL query complexity, and preferably wherein the SPARQL metric extractor extracts counts for different SPARQL verbs and/or SPARQL query malformation and/or language tags.
number: 3
text: A system according to claim 2, wherein the SPARQL metric extractor extracts SPARQL query complexity based on SPARQL syntax analysis.
number: 4
text: A system according to claim 3, wherein the queries are classified as light or heavy using a binary classifier trained on syntactic features of SPARQL queries, preferably wherein UNION statements, OPTIONAL statements, multiple joins, FILTER statements, and regular expressions in FILTERS indicate heavier complexity.
number: 5
text: A system according to any of claims 2 to 4, wherein the linked data-specific metric extractor further includes an RDF dereferencing interpreter to avoid double counting of access during an HTTP 303 redirect, and preferably wherein a request with an HTTP 303 redirect code together with a request with an HTTP 200 OK code for the same resource and visitor are counted as a single request if they originate within a certain time frame.
number: 6
text: A system according to claim 5, wherein any request with an HTTP 303 redirect code is added to a pending 303 list that keeps track of how many 303 patterns have been initiated, and which is labelled with the resource and visitor couple and if a request with an HTTP 200 OK code and the same label is detected with a timestamp that does not significantly differ from the HTTP 303 request, a separate pending 303 counter for the couple is decreased by 1, indicating that such 303 pattern is now concluded, and the request counter is incremented by 1.
number: 7
text: A system according to claim 6, wherein the resource is processed to apply the naming convention for the dataset, for example to strip the file extension, in order to match the resource names in the HTTP 303 request and the HTTP 200 OK code.
number: 8
text: A system according to any of the preceding claims, further including a general metric extractor to extract metrics related to web audience and/or a session detector to detect the number of visitor sessions.
number: 9
text: A system according to any of the preceding claims, wherein the request counter provides separate counts of HTTP and SPARQL queries.
number: 10
text: A system according to any of the preceding claims, wherein the request counter includes requests using SPARQL queries by counting the number of times a resource URI is included in SPARQL queries.
number: 11
text: A system according to any of the preceding claims, wherein the loading module includes a filter which discards requests using a UA blacklist and/or an IP-based blacklist and/or a domain-based blacklist.
number: 12
text: A system according to any of the preceding claims, wherein the linked data-specific metric extractor extracts one or more of the following metrics:
number: 13
text: A system for identifying abuses of a dataset of linked RDF data according to any of the preceding claims, wherein the SPARQL metric extractor is also to identify misuse of the dataset, for example by identifying unusual peaks of complex SPARQL queries.
number: 14
text: A decision support system for allowing improvements to datastore performance according to any of the preceding claims, wherein the system detects resources with more visitors than other resources and/or frequently executed SPARQL queries.
number: 15
text: A computer-implemented method of providing visitor information about visitor requests to a dataset of linked RDF data, the method comprising:
Office action: search_report, dated 2017-10-04
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: - KNUD MÖLLER ET AL: "Learning from Linked Open Data Usage: Patterns & Metrics", 26 April 2010 (2010-04-26), XP055358727, Retrieved from the Internet <URL:http://richard.cyganiak.de/2008/papers/lod-usage-websci2010.pdf> [retrieved on 20170324]
ref_type: npl
severity: background
ref_id: - MARKUS LUCZAK-RÖSCH ET AL: "Statistical Analysis of Web of Data Usage", 17 April 2012 (2012-04-17), XP055358933, Retrieved from the Internet <URL:http://web.archive.org/web/20120417152721/http://ceur-ws.org/Vol-784/evodyn1.pdf> [retrieved on 20170327]
ref_type: npl
severity: background
ref_id: - LUCA COSTABELLO ET AL: "Access Logs Don't Lie: Towards Traffic Analytics for Linked Data Publishers", 19 October 2016 (2016-10-19), XP055358685, Retrieved from the Internet <URL:http://ceur-ws.org/Vol-1690/paper61.pdf> [retrieved on 20170324]
ref_type: npl
severity: background
ref_id: - ANONYMOUS: "Web analytics - Wikipedia", 18 January 2016 (2016-01-18), XP055358736, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=Web_analytics&oldid=700390128> [retrieved on 20170324]
ref_type: npl
severity: background
ref_id: - BAMSHAD MOBASHER: "Chapter 12: Web Usage Mining", 24 August 2015 (2015-08-24), XP055358760, Retrieved from the Internet <URL:http://web.archive.org/web/20150824052125/http://facweb.cs.depaul.edu/mobasher/classes/ect584/Lectures/12-web-usage-mining.pdf> [retrieved on 20170324]
ref_type: npl
severity: background
ref_id: - Knud Möller ET AL, "Learning from Linked Open Data Usage: Patterns & Metrics", (20100426), URL: http://richard.cyganiak.de/2008/papers/lod-usage-websci2010.pdf, (20170324), XP055358727 [X] 1-3,5,8-15 * abstract * * page  2, column  left * * page  7, column  right * * Listing 1 * * page  7, column  left * * page  3, column  right * * tables 1-4 * * figures 4-9 * * page  6, column  right * * page  4, column  left * [I] 4,6,7
ref_type: npl
severity: novelty_destroying
ref_id: - Markus Luczak-Rösch ET AL, "Statistical Analysis of Web of Data Usage", (20120417), URL: http://web.archive.org/web/20120417152721/http://ceur-ws.org/Vol-784/evodyn1.pdf, (20170327), XP055358933 [A] 1-15 * Section 3; page  6  - page  10 *
ref_type: npl
severity: background
ref_id: - Luca Costabello ET AL, "Access Logs Don't Lie: Towards Traffic Analytics for Linked Data Publishers", (20161019), URL: http://ceur-ws.org/Vol-1690/paper61.pdf, (20170324), XP055358685 [XP] 1-15 * the whole document *
ref_type: npl
severity: novelty_destroying
ref_id: - Anonymous, "Web analytics - Wikipedia", (20160118), URL: https://en.wikipedia.org/w/index.php?title=Web_analytics&oldid=700390128, (20170324), XP055358736 [A] 1-15 * the whole document *
ref_type: npl
severity: background
ref_id: - Bamshad Mobasher, "Chapter 12: Web Usage Mining", (20150824), URL: http://web.archive.org/web/20150824052125/http://facweb.cs.depaul.edu/mobasher/classes/ect584/Lectures/12-web-usage-mining.pdf, (20170324), XP055358760 [A] 1-15 * page  1  - page  2; figure 1 *
ref_type: npl
severity: background
Final disposition: pending (2017-10-04)