Characterizing Machine Agent Behavior through SPARQL Query

MiningAravindan RaghuveerYahoo! Inc, Bangalore.

aravindr@yahoo-inc.com

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Introduction: LOD Users

The LOD cloud has two types of users- Humans (browsers). - Programs / machine agents.

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Introduction: LOD Access Methods

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The data on the LOD cloud can be accessed in multiple ways.

For this work, we categorize them into two buckets:- SPARQL : A powerful declarative graph query

language

- Non-SPARQL: Direct linked data requests.

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Motivation: User Behavior Understanding

Deep Understanding of client behavior can help build “better” serving systems

Better:- Secure- Scalable- Available

Prior Work:- Moller et al , WebSci 2010- Picalausa et al. Swim 2011- Kirchberg et. al Usewod 2011- Mario et. Al, Usewod 2011 4

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Summarizing. . .

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Human Users Machine Agents

Non-SPARQL

SPARQL This paper’s focus

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What this paper is about?

Mining of the USEWOD query log dataset to identify:

- Two Trends in Machine Agent Querying

- Two Patterns in Machine Agent Querying

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The USEWOD dataset

Query logs of servers hosting a part of LOD cloud data.

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Type # records(million)

% SPARQL

bio2rdf Life sciences ~ 0.2 100%

lgd Geo ~ 1.9 100%

SWDF Conference ~ 16.7 43.38%

dbpedia Structured wikipedia

~ 36.2 46.9%

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Part-1: Two Trends in Machine Agent Querying

The Theme

“What are the overarching trends for SPARQL queries?”

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Trend-1: SPARQL is here to stay!

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SWDF Dbpedia

Take-away: SPARQL query volume is pretty significant

0.1 – 1million

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Trend-2: SPARQL is heavily used by machine agents.

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Took 17 million user agents from SPARQL queries from dbpediaand..

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Part-2: Two Patterns in Machine Agent Querying

The Theme

“Looking at SPARQL query logs, can we reason about the program that generated the queries?”

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Salient aspects of proposed Query Mining Techniques

Move from per query analysis to query session analysis

Move from query analysis to query result analysis

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Pattern -1 : Loops in Programs

Take-away

• Through a per-user, temporal mining of logs, we discover patterns that are caused by loops in program.

• Significant support in all 4 datasets

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Per-user Temporal mining

14User-1 User-2 User-3 User-4

TIME

Original Logs

User level Session Analysis

Loop

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Intra Pattern Loop

successive queries from the same user, use the same “template”

Example: Two successive queries:

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SELECT * WHERE {http://bio2rdf.org/dr:D00332http://bio2rdf.org/ns/bio2rdf#xRefhttp://bio2rdf.org/cas:54-47-7}

SELECT * WHERE{http://bio2rdf.org/dr:D00333http://bio2rdf.org/ns/bio2rdf#xRefhttp://bio2rdf.org/cas:54-47-7}

Only the subject (D00332,D00333) varies

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Detecting Intra Pattern Loop

We convert a query to its canonical form by replacing variables, URI and literals by “keywords”.

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SELECT * WHERE {http://bio2rdf.org/dr:D00332http://bio2rdf.org/ns/bio2rdf#xRefhttp://bio2rdf.org/cas:54-47-7}

Canonical Form of the previous queries: SELECT * WHERE { _URI_ _URI_ _URI_ }

Queries generated by the same template will have the same canonical form.

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Salient Aspects of Intra Pattern loops

Iterate over a dictionary of values (categorical)

Iterate over a numerical range (example LIMIT, OFFSET parameters in SPARQL queries)

Multiple levels of nested loops with the same intra loop pattern.

4 Parameters to quantify above (in paper)17

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Inter Pattern Loops

Found loops that iterate over a set of patterns

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P1,P2,P3 ,P1,P2,P3,P1,P2,P3

Typically used when the output of the first query goes as a parameter to the second query.

(examples in paper)

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Results

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86% 32%

40% 16%

Take-away:Significant support

for loops!bio2rdf

lgd

swdf dbpedia

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Pattern-2: Querying for dbpedia Linkage

Take-away:• By executing each query • analyze the results, we find that a portion of

queries “look” for dbpedia links• Results:- 20 months of SWDF queries had average of 8% look

for dbpedia urls- 2 days worth of lgd queries had 26.5% queries look

for dbpedia urls

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Summary & Conclusions

Proposed 2 new ways of SPARQL query mining:- Session view - Analyze results in addition to query

Showed that machine agents look for dbpedia using the owl:sameas annotation.

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Influence on system design:- Can we pre-fetch elements in loop beforehand?- Priortitize dbpedia attributes for caching

Influence on log collection & analysis:- Stratified random sampling to remove effect of loops.

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For the great data !! For the great feedback & commentsFor listening!

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The famous LOD Cloud . . .

7 billion triples and counting!!23