Welcome to
 
Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN 2278 - 6503
Online ISSN 2278 - 6511


  About JIO
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
Journal of Multimedia Processing and Technologies (JMPT)
International Journal of Web Application (IJWA)

 

 
Journal of Information Organization 
Volume: 15, Issue: 2 (  June   2025)
Editorial Message
Research
Quotient-Based Approaches for Semantic Graph Summarization: Techniques, Applications and Future Directions
Ansgar Scherp, David Richerby, Till Blume, Michael Cochez, Jannik Rau
Page: 53-84
Abstract   Full Text    Download: 2 times
https://doi.org/10.6025/jio/2025/15/2/53-84
Abstract: The paper explores advanced techniques for summarising semantic graphs, where data points and their relationships are labelled and potentially carry semantic meaning. Given graph-structured data’s growing size and complexity, summarization becomes essential to enable efficient data processing and querying. The authors introduce semantic graphs as labeled, directed graphs, typically used in RDF (Resource Description Framework) or labeled property graph formats. These graphs represent relationships in domains like social networks, bioinformatics, and the Semantic Web. The central idea is that large graphs can be abstracted through graph summaries, which retain necessary structural features while omitting irrelevant details. The paper emphasizes quotient-based structural summaries, where graph vertices are grouped into equivalence classes based on structural similarity. These summaries are either lossless or lossy, depending on whether they preserve all information relevant to a particular task. Equivalence relations underpinning summaries can use attributes like vertex labels, edge labels, or neighborhood structures. Stratified kbi simulation—a method for defining similarity based on paths of limited length—is widely used. Four main applications are detailed: (1) semantic entity retrieval, (2) cardinality estimation, (3) data source indexing, and (4) training graph neural networks. The paper further distinguishes summarization from compression and contraction, clarifies how summary models and payload functions tailor summaries to specific tasks, and discusses logic-based frameworks and extensions to temporal graphs. In conclusion, we outline future directions including multi-summaries, learned task-specific summarisation, GPU acceleration, and a standardised library for graph summarisation.
Enhancing Distributed Service Composition through Adaptive Multi- Objective Optimization
Dionysios Efstathiou., Peter McBurney, Noël Plouzeau, Steffen Zschaler
Page: 85-92
Abstract   Full Text    Download: 0 times
https://doi.org/10.6025/jio/2025/15/2/85-92
Abstract: The paper addresses improving the quality of distributed composite service applications in dynamic, service- oriented environments. It highlights the limitations of existing approaches that focus only on individual service quality, ignoring factors like coordination, communication, and interaction among services. Using a fire-fighting support system as a case study, the authors advocate for a multi-objective, self-adaptive optimization approach considering multiple “degrees of freedom.” They propose using evolutionary algorithms to generate high-quality compositions at runtime guided by dynamic user preferences. Future work involves developing a meta-model and evaluating centralized versus distributed optimization strategies.
A Public Platform for Analysing Battery use with Android Devices
Gareth L. Jones, Peter G. Harrison
Page: 93-99
Abstract   Full Text    Download: 1 times
https://doi.org/10.6025/jio/2025/15/2/93-99
Abstract: The paper presents Open Battery, a publicly accessible platform for collecting and analyzing battery usage data from Android devices. The authors developed an app that logs battery state changes and uploads the data for analysis. Observations from 20 smartphones over three months reveal variability in logging behavior and device inconsistencies. The study introduces fluid queues as a modeling tool to stochastically represent charging/discharging patterns. The model allows for improved understanding of battery behavior and supports future development of predictive tools for battery life estimation. Future work includes refining the model for level-dependent behavior and analyzing long-term battery performance degradation.
Notification
Book Review
Research Writing Blog
Sixth International Conference on Science & Technology Metrics (STMet 2025)
Fifth International Conference on Digital Data Processing (DDP 2025)
Third International Conference on Modelling and Forecasting Global Economic Issues (MFGEI 2025)
Seventh International Conference on Real-time Intelligent Systems (RTIS 2025)


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright 2011 dline.info