NDLI: The Bayeslet Concept for Modular Context Inference

Content Provider	IEEE Xplore Digital Library
Author	Frank, K. Rockl, M. Robertson, P.
Copyright Year	2008
Description	Author affiliation: German Aerosp. Center (DLR), Inst. for Commun. & Navig. Oberpfaffenhofen, Wessling (Frank, K.; Rockl, M.; Robertson, P.)
Abstract	In the development and design of ubiquitous computing many challenges are arising. While there is much research done on service management systems and context provisioning, less effort is spent on the methods to actually generate context information - the process of context inference. If we are considering this field of research, we have not only to consider the pure algorithmic problem to infer otherwise unknown information from available data, we also have totarget the challenges of large scale systems with millions of users possibly spread across the world and the user's requirements who is neither willing nor able to wait more thana couple of seconds for his request to be served. In this work we consider shortcomings in today's context inference systems and analyze requirements for emerging architectures relying on probabilistic algorithms, more precisely static Bayesian networks. We postulate the fragmentation of large networks into smaller so called Bayeslets, that are modular, (un)pluggable, individualisable and easy to process, as they are small and processing can be parallelised. Further on, we propose a formalism to note those Bayeslets in the Bayeslet Language (BalL). Hence, we have a way to easily exchange and deploy Bayeslets and even give application developers a way to provide their own inference rules to the pervasive system.
Starting Page	96
Ending Page	101
File Size	174472
Page Count	6
File Format	PDF
ISBN	9780769533674
DOI	10.1109/UBICOMM.2008.53
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-29
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering plug & play BalL modular Probabilistic logic Ubiquitous computing distributed inference Bayesian Networks Bayeslet description language context inference Bayesian methods Prototypes XML Bayeslet Random variables
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Inference with Aggregation Parfactors: Lifted Elimination with First-Order d-Separation

Optimizing dynamic composition of Bayesian Networks for context sensing and inference

Parallel sampling in Bayesian networks

P-CLASSIC: A tractable probabilistic description logic (1997)

P-CLASSIC: A tractable probabilistic description logic (1997)

P-CLASSIC: A tractable probabilistic description logic (1997)

Probabilistic ontologies for knowledge fusion

Optimizing dynamic composition of bayesian networks for context sensing and inference (2010)

A parallel algorithm for exact Bayesian network inference

The Bayeslet Concept for Modular Context Inference

Similar Documents

Inference with Aggregation Parfactors: Lifted Elimination with First-Order d-Separation

Optimizing dynamic composition of Bayesian Networks for context sensing and inference

Parallel sampling in Bayesian networks

P-CLASSIC: A tractable probabilistic description logic (1997)

P-CLASSIC: A tractable probabilistic description logic (1997)

P-CLASSIC: A tractable probabilistic description logic (1997)

Probabilistic ontologies for knowledge fusion

Optimizing dynamic composition of bayesian networks for context sensing and inference (2010)

A parallel algorithm for exact Bayesian network inference

The Bayeslet Concept for Modular Context Inference