=Paper=
{{Paper
|id=Vol-197/paper-4
|storemode=property
|title=Applying Inference Engine to Context-Aware Computing Services
|pdfUrl=https://ceur-ws.org/Vol-197/Paper4.pdf
|volume=Vol-197
}}
==Applying Inference Engine to Context-Aware Computing Services==
https://ceur-ws.org/Vol-197/Paper4.pdf
ubiPCMM06:2nd International Workshop on Personalized Context Modeling and Management for UbiComp Applications
Applying Inference Engine to Context-Aware Computing
Services
Jaemoon Sim, Jihoon Kim, Ohbyung Kwon Sean S. Lee, Jungho Kim, HK Jang,
College of Management and International Relations Myungchul Lee
KyungHee University IBM Ubiquitous Computing Lab
Yongin, Kyungggi-do, 449701, South Korea Dogok-dong, Kangnam-gu, Seoul, Korea Rep 135700
+82-31-201-2306 +82-2-3781-8598
{ deskmoon, hdlamb, obkwon}@khu.ac.kr { lsean, kjungho, hkjang, mclee}@kr.ibm.com
ABSTRACT full-fledged ubiquitous computing services covering vast zones
Ubiquitous computing services started taking advantage of the with various sets of requirements such as lots of fast moving users
reasoning capabilities of inference engines to acquire hidden and and other transient computing entities. The main purpose of this
potentially useful contextual information. However, performance paper is to examine how well inference engines satisfy
evaluations of the inference engines have been limited to the responsiveness requirement, scalability requirement and
domain of static information reasoning; evaluations of requirement to accommodate frequent inference requests in
requirements pertaining to ubiquitous computing environment response to dynamic data insertion and deletion that realistic
have been largely neglected. This paper aims to examine how ubiquitous computing environments exhibit. To do so, we have
different types of inference engines perform by applying them to modeled scenarios based in a major Korean university such as
realistic ubiquitous computing scenarios. Based on the scenarios, MyEntrance service. MyEntrance service scenario is a part of a
three measurement criteria are proposed and measured including larger Celadon project [8] in aim to study and adopt the most
scalability as data set gets large, responsiveness for user’s requests, suitable reasoner for ubiquitous environment in its system.
and adaptability to frequent inference requests. Specifically, five most prominent engines are considered based on
their reasoning mechanisms: three memory-based and two DBMS-
Keywords based engines.
Inference Engine, OWL-DL, scalability, MINERVA, DLDB-OWL
2. INFERENCE ENGINES
1. INTRODUCTION MINERVA, JENA, Pellet, RacerPro, and DLDB-OWL (HAWK)
Ubiquitous computing services aim to provide information and [1, 2, 3, 4, 5, 6, 7] are discussed as representative instances of each
services in more intelligent ways with more seamless interfaces class of reasoners and they are summarized in Table 1.
that aid users to be conveniently served anytime, anywhere with
any devices without awkward user intervention. These must rely 2.1 MINERVA
on not only multiple sensors capturing user’s context but also Minerva is high performance OWL storage, inference, and query
reasoning capabilities for processing raw sensed context data into system built on RDBMS. The advantages of the system could be
more useful and meaningful information within the user’s categorized in two important aspects of a reasoner: response time
environment. Recently, inference engines such as Jena, RacerPro, and scalability. It does all required inferences in the time of data
FacT++, and Minerva have been proposed as a core component of load instead of data query, making it more responsive at the time
intelligent ubiquitous computing systems. There had been many of user query. In addition, it calculates all inferences in relational
extensive evaluations of their reasoning capabilities in correctness, database management system, making it more scalable than
completeness, and response time [10, 11]. It is less widely known memory based counterpart. It is provided as a component of
without fully analyzing how well the inference engines can fare IBM’s Integrated Ontology Development Toolkit (IODT) [4] and
it supports DLP (Description Logic Program), a subset of OWL
Permission to make digital or hard copies of all or part of this work for DL and conjunctive query, a subset of the SPARQL language.
personal or classroom use is granted without fee provided that copies are Minerva uses Description Logic reasoner for TBox and a set of
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise,
logic rules translated from Description Logic Programming (DLP)
or republish, to post on servers or to redistribute to lists, requires prior for ABox inference.
specific permission and/or a fee.
2.2 DLDB-OWL/HAWK
UbiPCMM06 September 18, 2006, California, USA
ubiPCMM06:2nd International Workshop on Personalized Context Modeling and Management for UbiComp Applications
Table 1. The Comparative Table for Inference Engine
Import Inference Repository Query
Version Source
Ontology Internal External Persistent Memo
Capability Language
Language Engine Engine Storage -ry
Semantics
DB2, Derby,
Minerva OWL DL O Racer, Pellet X SPARQL Toolkit- IBM
HSQLDB
1.1.1
HAWK 1.3 Lehigh
DLDB- MS-Access, KIF-Like
OWL DL X Racer O beta University
OWL PostgreSQL language
SWAT Lab
DIG tell DIG ask
Pellet document, DL O X X O document, pellet-1.3 mindswap
OWL RDQL
DIG ask Racer
DIG tell
document, RacerPro Systems
Racer document, DL O O X O
Racer Query 1.9.0 GmbH & Co.
OWL
Language KG
Racer Pro, MySQL,
Pellet, HSQLDB,
JENA OWL DL O Fact++ PostgreSQL, O SPARQL Jena 2.4 HP Labs
(JenaOntMo Oracle, MS-
del) SQL Server
DLDB-OWL is a repository framework and toolkit that supports It provides a programmatic environment for RDF, RDFS and
OWL It provides APIs as well as implementations for parsing, OWL, SPARQL and includes a rule-based inference engine. The
editing, manipulating and preservation of OWL ontologies. The Jena Framework includes a RDF API, reading and writing RDF in
architecture of DLDB-OWL consists of three packages: core, owl RDF/XML, N3 and N-Triples and an OWL API and SPARQL
and storage. The core package defines the generic interfaces of the query engine.
data structures of ontology and ontology objects, e.g. class and
property. The core package, that is independent of underlying 3. PERFORMANCE EVALUATION
model the application will use, provides an API for constructing
and manipulating ontology models. The owl package provides the 3.1 MyEntrance Service Scenario
utilities for parsing and serializing ontologies in OWL language. To analyze how the legacy inference engines perform in realistic
ubiquitous computing environments, we used a MyEntrance
2.3 Pellet service scenario modeled based on Kyunghee Univerity (KHU).
Pellet is an open-source Java based OWL DL reasoner. It can be KHU has two great campuses, Seoul campus with 50 departments
used in conjunction with both Jena and OWL API libraries and and YongIn campus with 51 departments.
also provides a DIG interface. Pellet API provides functionalities “When a member of KHU enters the Student Union Building, a
to see the species validation, check consistency of ontologies, service agent recognizes the member’s preference and searches for
classify the taxonomy, check entailments and answer a subset of an Event on the Application Server to provide it for him/her.
RDQL queries. It supports the full expressivity OWL DL including Additionally, a sports equipment shop located in Student Union
reasoning about nominals (enumerated classes). Building wants to advertise its sales promotion on new baseball
products for the KHU students and faculty members. The Service
2.4 RacerPro Agent of the sports equipment shop in KHU requests Application
RacerPro is an OWL reasoner and inference server for the Server to retrieve the information on the hobbies of members
semantic web. The origins of RacerPro are within the area of located in the Student Union Building. The Application Server
description logics. It can be used as a system for managing hands over the request of Service Agent to Context Server about
semantic web ontologies based on OWL. However, RacerPro can the hobby information of members who are located in the Student
also be seen as a semantic web information repository with Union Building. Context Server returns the result of preference
optimized retrieval engine because it can handle large sets of data and product matching inference. Consequently, the Service Agent
descriptions. checks for the members who like to play baseball and it sends the
Discount Event Information to appropriate members. The
2.5 JENA members who receive the event information make a purchase
Jena is a Java framework for building Semantic Web applications. decision with the received offer.
ubiPCMM06:2nd International Workshop on Personalized Context Modeling and Management for UbiComp Applications
To accommodate this scenario, we have developed KHU campus
Application server
ontology based on the ontology generation program supported by
(context ontology reader)
IBM China Lab [9]. On top of the generated ontology, we have (Inference engine)
installed area-based location context. An experimental data set
which represents an actual college (9 departments, file size=
11.8MB), International College in KHU at Suwon, was made and Client
used in the performance evaluation.
3.2 Results: Performance Evaluation on Static
and Dynamic Context Information
For the performance evaluation on static information, we placed Context event handler
(context ontology writer)
our focus on scalability and subsequent performance issue. Context server
Specifically, in evaluating the performance of query processing, (OWL DL ontology)
we considered 16 sets of University Ontology Benchmark [9]
queries that were generated by IBM China Research Lab. by Figure 1. Simulation Environment
extending widely used Lehigh University Benchmark [10].
In order to evaluate handling of context information, SPARQL is We select query response time as the performance measure. The
used as follows: summary of response time is listed in Table 2.
SELECT Table 2. Summary of query response time
DISTINCT ?person ?zone ?Hobby1 ?Hobby2 ?Hobby3 Response Time (ms)
WHERE DLDB-
Query #
(?person benchmark:locatedIn MINERVA OWL Pellet
) 1 424.90 10.00 5858.00
(?person benchmark:locatedIn ?zone) 2 312.80 12.85 268.70
(?person benchmark:like ?Hobby1) 3 284.30 241.85 115.50
4 382.80 221.57 89.00
For DLDB-OWL/HAWK evaluation, the above query is translated 5 358.00 2.85 31.20
into KIF-like query as follows: 6 343.70 5.71 42.20
7 483.00 5.71 40.50
[http://rcubs.kyunghee.ac.kr/owl/univ-bench-
8 743.60 5.71 1813723.00
dl.owl]
9 843.90 160.14 295.40
(type Person ?x)
10 857.90 25.71 7.80
(locatedIn ?x
11 81.71 17.40
http://rcubs.kyunghee.ac.kr/owl/rcubs-univ-bench-
12 1176.60 43.00 9.20
dl.owl#Zone1)
13 906.40 2.85 1199564.80
(locatedIn ?x ?z)
(like ?x ?y1)
14 1076.60 173.00 467724.80
15 978.10 158.71 208571.80
We set up our scenario environment of campus like the topology 16 1186.00 291.71 51.50
shown as Figure 1. For context generation, user’s current location Performance evaluations in case of dynamic context were also
is gathered by the context event handler on the client’s portable performed. Dynamic context evaluations were differentiated from
device or tags at any time the user is passing by sensors located in the static context evaluation in that, engines were constantly
the entrance gate of the campus buildings. At a fixed cycle, the requested to inference based on new data whereas the static
context event handler sends the user’s current context information evaluations were performed after the inference processing is
as OWL-DL format to the context server. The context server finished. For this test, since the memory-based reasoning systems
returns the result if the ontology files comes through successfully cannot be directly compared to the DBMS-based reasoning
to the context event handler. The user may invoke the application systems in their absolute loading time, we tested two database-
server to get location-based service at any time the user wants. based reasoning systems: MINERVA and DLDB-OWL/HAWK.
Then the application server asks the context server to get service- Only the test with data set 1 is described in this paper because we
related context data as facts. According to the facts, the inference encountered a consistent problem with the dynamic data set and
engine determines the location-based services most appropriate for did not see the value in presenting more data in this study. The
the user. result is listed in
ubiPCMM06:2nd International Workshop on Personalized Context Modeling and Management for UbiComp Applications
Table 3. Summary of load time in dynamic situation and third numbers denote the rate of yielding wrong answers and
no response to the query, respectively.
Load Time (%)
4. CONCLUSION
Cycle time of Cycle time of DLDB- We first studied the responsiveness to the user request using both
Minerva
updating query OWL memory-based and DBMS-based inference engines in static
context data processing context setting. The result indicated that database-based inference
33.3 99.8
600sec 0.0 0.2 engines far outperform the other in this criterion owing to the fact
66.7 0.0 that memory based inference engines pre-process context data
while loading and thus is able to respond without further
5.0 89.5
calculation at query time.
1200 SEC 60sec 6.7 10.5
Then, using DBMS-based inference engines, we analyzed how
88.3 0.0
they perform in conducting a context-aware MyEntrance service
0.6 91.3
under the setting and environment of a major university. By
10sec 3.6 8.7
varying the cycle of context changes and knowledge loading,
95.8 0.0
performance measures such as correctness and completeness were
33.3 93.3
examined. We noticed that engines do not respond to queries in the
600sec 16.7 6.7
middle of their inferences and sometimes generate invalid or no
50.0 0.0
responses.
5.0 84.0
We conclude that current state-of-the-art inference engines do not
900 SEC 60sec 6.7 16.0
fulfill responsiveness, scalability and high-update frequency
88.3 0.0
requirements demanded for ubiquitous computing environments
0.8 89.1
with lots of fast moving users and other transient computing
10sec 6.7 10.9
entities. And, evolutionary algorithms or inference mechanisms to
92.5 0.0
deal with enormous amount of data, frequency of updates and
50.0 90.0
respond to user’s needs in time are needed for the inference
600sec 0.0 10.0
engines to be improved.
50.0 0.0
6.7 71.0 5. ACKNOWLEDGEMENT
600 SEC 60sec 5.0 29.0 Thanks to the Institute of Information Technology Assessment and
88.3 0.0 Ministry of Information and Communication of Republic of Korea
0.8 85.7 for providing an opportunity to participate in the IT839 project.
10sec 6.7 14.3 And, thanks to IBM China Research Laboratory for providing
92.5 0.0 detailed information about Integrated Ontology Development
Toolkit available on IBM AlphaWorks.
Table 3 where the first number of the three denotes the rate of
yielding correct answers to the query aforementioned. The second
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