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Two Postdoc Openings in Social Network/Semantic Web

Two Postdoc Openings in Social Network/Semantic Web

Laboratory for Systems, Software and Semantics (LS3)
Ryerson University
Toronto, Canada

We are seeking outstanding candidates for two Postdoctoral Fellow positions to strengthen our research program in semantics-enabled social media analytics. Our research platform focuses on developing technology for information extraction from social media/networks.

Candidates should fit the following criteria:

    -PhD in Computer Science, Information Systems, Informatics, or relevant subjects.

    -Evidence of participation in research grants acquisition (national and international, from public bodies or from industry) and the ability to help lead a research team.

    -Minimum two years software development experience with strong Java or Python programming skills.

    -Knowledge of software design, development and maintenance processes.

    -Have experience in some, if not all, of the following:
    * Social Media Mining
    * Natural Language Processing
    * Social Network Analysis
    * Semantic Web and Linked Data technologies
    * Stream processing

    -Proven ability to work independently and in a team environment.

    -Be creative and enthusiastic, with excellent communication skills.

The ideal candidate will have excellent research capabilities as well as competitive development skills. Salary and benefits are competitive and commensurate with expertise. Both positions are for one year with the possibility of extension for a second year.

Further inquiries may be directed to Dr. Ebrahim Bagheri (

Interested candidates should submit an application cover letter, a résumé, three relevant publications and contact information of three reference persons to Dr. Ebrahim Bagheri ( Candidates will be screened until the position is filled.

The Laboratory for Systems, Software and Semantics (LS3) is a part of the Faculty of Engineering, and Architectural Sciences at Ryerson University. Ryerson University is a public research university located in downtown Toronto, Ontario, Canada. Its urban campus surrounds the Yonge-Dundas Square, located at the busiest intersection in downtown Toronto. The university is composed of 36,000+ undergraduate students, 2,000+ graduate students, and 70,000 yearly certificate and continuing education registrations. Ryerson has been one of the fastest growing research institutions in Canada.

Special Issue on Software Engineering for Big Data

Special Issue on
Software Engineering for Big Data 
Elsevier’s Information and Software Technology Journal 

The main focus of this special issue will be to attract high quality publications pertaining to the development and provisioning of appropriate software engineering methods, techniques, processes, and tools that would support the efficient and effective development and maintenance of Big Data applications through the different stages of the software development lifecycle.

It has been widely reported that 90% of all the systematically collected data in the world has been generated over the past two years. This significant amount of data, being accumulated on a constant basis, comes from a fair share of user generated content as well as from corporate and industrial entities such as banks, internet, healthcare and transit systems, to name a few. There is immense value in harnessing data of this scale. The 2011 McKinsey report estimates that by implementing big data solutions, the US healthcare sector alone would benefit $300 billion annually. Substantial effort has thus already been dedicated to building large-scale data platforms, e.g., in-memory databases, distributed data processing architectures, and stream-processing tools, which facilitate the production or consumption of big data. However, it is time to surge ahead with advancements in the area of building complex application systems centred around Big Data.

In the past, the software and services sector has been struck powerfully by the lack of objective measures for undertaking large-scale software development. For example, it was reported by the Standish group in 2001 that over 84% of software projects either failed or became severely over-budget or over-time, wasting over $200B per year in North America. If history repeats, it is not difficult to estimate that the domain of Software Engineering in the context of Big Data will face great challenges with high-stake investments in mission critical application areas.

Important questions that need answering include, but are not limited to:

  • What new requirements engineering modelling, specifications, and analysis techniques are needed to deal with the domain of Big Data and in the context of multiple stakeholders?
  • What example reference architectures suit specific domain applications for Big Data systems — a product issue; and what is the role of reference architectures in rapid development of novel Big Data application systems — a process issue?
  • What new scalable approaches are needed for testing Big Data application systems in the lab?
  • What new architecture and design patterns are suited to specific qualities of Big Data application systems?
  • What new programming paradigms better handle the programming of Big Data system components than traditional paradigms?
  • What new kinds of development time and runtime tools are envisaged for improving the development and maintenance of Big Data systems?
  • What new metrics are needed to measure or predict the quality, cost and timeframes of Big Data applications?
  • How can we leverage operational Big Data generated by the Big Data Systems?
  • How autonomic or self-adaptive systems and infrastructures can play a role on engineering and deploying efficient Big Data applications?

We solicit high quality, original papers that advance the state-of-the-art, and open new research directions in the area of Software Engineering for Big Data. The submissions, which are anticipated to be of scientific writing, may focus on research-theoretical work, evidence-based empirical studies including industrial experience, case studies and action-research, systematic literature reviews, and the like. Strong sections on Motivation, Related Work, claims of originality, Methodology, Analysis, Discussion, Interpretation, Validation, Implication, Conclusion, and Future Work are expected as applicable to the type of paper submitted.

Tentative Timeline:
· Submission Deadline: 1-March-2016
· Notification: [1 July, 2016]
· Major Revisions Due: 1-Sept-2016
· Re-reviews Completed: 1-Nov-2016
· Minor Revision Due: 1-Dec-2016
· Final recommendations: 15-Dec-2016

Authors will need to submit their manuscripts through the online submission and editorial system for Information and Software Technology Journal accessible at When submitting the manuscript for this special issue, please select “SI: Software Engineering for Big Data” as the Article Type.

Guest Editors:
Ebrahim Bagheri, Kostas Kontogiannis, Nazim Madhavji, Andriy Miranskyy.

LS3 participated in Ryerson Graduate Student Symposium

This event took place in atrium of Electrical and Computer Engineering department at Ryerson University on November 21st of 2014, hosted by Ryerson University, which gain the interest of many professors, graduate and undergraduate students from and around Toronto. LS3 members presented three posters in this event which attracted the attention of many researchers participated in the event. Many of the participants and visitors of the event took advantage from the helpful comments that were exchanged between them. The three posters were:

1. Semantically Latent Community Detection
Authors: Manish Varma, Hossein Fani, Ebrahim Bagheri

2. Computing Semantic Relatedness by Mining Twitter Space
Authors: Luna Feng, Ebrahim Bagheri

3. Semantics-enabled Search Query Intent identification
Authors: Andisheh Keikha, Faezeh Ensan, Fatemeh Lashkari, Ebrahim Bagheri

More information about this event can be found here:





LS3 presented some of its research at CASCON 2014

IBM hosted its annual Centre for Advanced Studies Conference (CASCON) at Hilton Suites in Markham, Ontario. In this conference, academia and industry partners demonstrate their latest development of advanced software technologies in a technology showcase as well as series of paper presentations and workshops. LS3 presented its latest development in the Moviesion project in CASCON technology showcase. It also presented some of its research in two paper presentation sessions.

Our paper “Mining Common Morphological Fragments from Process Event Logs” by Asef Pourmasoumi,Mohsen Kahani, Ebrahim Bagheri, and Mohsen Asadi won the Best Student Paper Award.

More information about IBM CASCON can be found here:





LS3 hosted the first of its research-day colloquium series

LS3 hosted the first of its research-day colloquium series on Wednesday May 7th 2014. LS3 Researchers shared the highlights of their research with their colleagues at the laboratory for systems, software and semantics by preparing a presentation about their research.

LS3 members

The presenter focused on Research Domain, Motivation for Work, Contributions, and Outline of their Solutions. The members shared their thoughts with presenters after each presentation.

The presentation titles and slides can be found below:

“Engineering Self-adaptive Service Mashups” (Mahdi Bashari)
“Metrics-Driven Approach for LOD Quality Assessment” (Behshid Behkamal)
“Semantic Search” (Andisheh Keikha)
“Indexing and retrieval for semantic search” (Fatemeh Lashkari)
“Non-functional Properties in Software Product Lines: A Framework for Developing Quality-centric Software Products” (Mehdi Noorian)
“DERIVE – Assigning properties to objects within natural language text” (John Cuzzola)

Tutorial Presentation: Engineering Self-Adaptive Systems and Dynamic Software Product Line

Presented at 17th International Software Product Conference (SPLC 2013).

Self-adaptive systems are a class of software applications, which are able to dynamically transform their internal structure and hence their behavior in response to internal or external stimuli. The transformation may provide the basis for new functionalities or improve or maintain non-functional properties in order to match the application better to its operational requirements and standards. Software Product Line Engineering has rich methods and techniques in variability modeling and management which is one of the main issues in developing self-adaptive systems. Dynamic software product lines (DSPL) have been proposed to exploit the knowledge acquired in SPLE to develop self-adaptive software systems.

In this tutorial, we portray the problem of developing self-adaptive systems. Then we investigate how the idea of dynamic software product line could help to deal with the challenges that we face in developing efficient self-adaptive software. We also offer insight into the different approaches that use dynamic software product line engineering for developing self-adaptive systems focusing on practical approaches by showing how the approaches are applied to real case studies and also methods for evaluating these approaches. This tutorial also discuss how DSPL could be used some relevant areas to self-adaptive systems and challenges which still exist in the area.

Mahdi Bashari, Ebrahim Bagheri


Special issue on Empirical Evidence on Software Product Line Engineering



Empirical Software Engineering:

An International Journal

Special issue on

Empirical Evidence on Software Product Line Engineering

Empirical Software Engineering Journal



Software product line engineering (SPLE) is a paradigm that advocates the reusability of software assets and the rapid development of new applications for a target domain. These objectives are achieved by capturing the commonalities and variabilities between the applications of a target domain and through the development of comprehensive and variability-rich models. Software product line engineering practices offer desirable characteristics such as rapid product development, reduced time-to-market, quality improvement, and more affordable development costs as a result of a systematic management of the variabilities in a set of products (the product line) leading to methodical reuse of software assets. In light of these achieved benefits, the software product line community has received very significant attention both in research and in industry. However, a recent systematic literature review [1] showed that only a limited portion of the proposed work actually offers serious or effective empirical evidence regarding the appropriateness, usability and/or effectiveness of their work. This special issue seeks submissions in areas pertaining to empirical evidence of success or failure of software product line or emerging software product line approaches.  Contributions in the following forms are especially encouraged:

  • Industrial experience reports, case studies and action-research in SPL
  • Applied SPL approaches with a strong empirical component
  • New research methods for evaluating SPL research
  • Measurement, comparison and development of empirical methods in SPL
  • Studies on SPL technology transfer to industry
  • Process improvement in SPLs supported by empirical evidences
  • Longitudinal studies of SPL evolution over time
  • Cost-benefit analysis for SPLs in practice


  • Submission Deadline:                      7 Feb 2014
  • First Notification:                        10-April-2014
  • Major Revisions Due:                      10-May-2014
  • Second Notification:                       1-July-2014
  • Minor Revision Due:                       20-August-2014
  • Final Recommendations:                10-November-2014

PAPER SUBMISSION DETAILS – Authors are invited to submit original contributions to Authors should state in their cover letter to the EiC that “This manuscript is submitted for the special issue on Empirical Evidence in Software Product Line Engineering “. Submission instructions can be found under “Information for Authors” at:

GUEST EDITORS (alphabetical order)

  • Ebrahim Bagheri, Ryerson University, Canada
  • David Benavides, University of Seville,Spain
  • Per Runeson, Lund University, Sweden
  • Klaus Schmid, University of Hildesheim, Germany


[1] Alvin Ahnassay, Ebrahim Bagheri, Dragan Gasevic, Empirical Evaluation in Software Product Line Engineering, Technical Report, Laboratory for Systems, Software and Semantics, Ryerson University, TR-LS3-130084R4T, 2013. Link