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| 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. |
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