Category Archives: News

Postdoc/Research Associate in Web Search/Social Network Analytics

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

http://ls3.rnet.ryerson.ca/

Post-Doctoral or Research Associate Position

We are seeking a Postdoctoral Fellow or Research Associate to work on social media/network analytics and/or web search. The ideal candidate will have excellent research capabilities as well as competitive development skills.

The candidate should have expertise in data mining, machine learning, social network analytics or web search. A PhD in Computer Science or related fields is required. Expertise with NoSQL technology, Java, Javascript and frameworks for distributed processing in Java (e.g. Apache Spark, Hadoop MapReduce) will be an asset.

Salary and benefits are competitive and commensurate with expertise. The position is for one year with the possibility of extension.

Further inquiries may be directed to Dr. Ebrahim Bagheri (bagheri@ryerson.ca).
Interested candidates should submit an application letter, a résumé, three publications relevant to the post-doctoral project and contact information of three reference persons to Dr. Ebrahim Bagheri (bagheri@ryerson.ca ). 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.

Ryerson University values diversity and is committed to equal opportunity in employment.

International Workshop on Mining Actionable Insights from Social Networks

International Workshop on Mining Actionable Insights from Social Networks
MAISoN 2017: http://ls3.rnet.ryerson.ca/MAISoN/2017/

Co-located with Tenth ACM International Web Search and Data Mining (WSDM) Conference in Cambridge, UK February 6-10, 2017.
http://www.wsdm-conference.org/2017/

The wide adoption of social network churns out an ocean of data which presents an interesting opportunity for mining the data and discover new knowledge to predict real-world outcomes. The enormity and high variance of the information that propagates through large user communities influence the public discourse in society and sets trends and agendas in topics that range from marketing, education, business and medicine to politics, technology and the entertainment industry. Mining the attributes and contents of social network provides an opportunity to discover social structure characteristics, analyze action patterns qualitatively and quantitatively, and the ability to predict future events. In recent years, decision makers have become savvy about how to translate social data into actionable information in order to leverage them for a competitive edge. In particular, marketers aggregate the opinions of the collective population to dynamically calibrate, anticipate and offer products and services that meet perpetually shifting consumer demands in a hyper-competitive marketplace. The traditional research in social network mainly focus on theories and methodologies on community discovery, pattern detection and evolution, behavioural analysis and anomaly and misbehaviour detection. The main distinguishing focus of this workshop will be the use of social media data for building predictive models that can be used to uncover hidden and unexpected aspects of user-generated content in order to extract actionable insight from them. The objectives will be to transform the insight into effective actions which could help organizations improve and refine their strategies.

In this workshop, we invite researchers and practitioners from different disciplines such as computer science, big data mining, machine learning, social network analysis and other related areas to share their ideas and research achievements in order to deliver technology and solutions for mining actionable insight from social network data.

Topics
We solicit original, unpublished and innovative research work on all aspects around, but not limited to, the following themes:
• Applications of Social Network Analysis
• Predictive modeling based on social networks such as
◦ Box office prediction
◦ Election prediction
◦ Flu prediction
• Product adaptation models with social networks such as
◦ Sale price prediction
◦ New product popularity prediction
◦ Brand popularity
◦ Business downfall prediction
• User modeling and social networks including
◦ Predict users daily activities including recurring events
◦ User churn prediction
◦ Determining user similarities, trustworthiness and reliability
• Social networks and information/knowledge dissemination
◦ Topic and trend prediction
◦ Prediction of information diffusion patterns
◦ Identification of causality and correlation between event/topics/communities
• Information diffusion modeling with social networks
◦ Sentiment diffusion in social network
◦ Competitive intelligence from social networks
• Merging internal (proprietary) data with social data
• Search Behaviour Analytics with Social Networks
• Feature Engineering from Social Networks
• Social Networks and Recommender Systems
• Sentiment Analysis and Prediction on Social Networks
• Datasets and Evaluation methodologies for predictive modeling in social networks

Format and Submission
We invite the submission of regular research papers (6-8 pages) as well as position papers (2-4 pages). We recommend papers to be formatted according to the standard double-column ACM Guidelines. All papers will be peer reviewed by three reviewers. 
All submissions must be submitted in PDF format according to the guidelines through the Easychair installation: https://easychair.org/conferences/?conf=maison2017

Important Dates
• Submission: November 10, 2016
• Decisions: December 5, 2016
Submissions will be due Midnight AoE time.

Organizers
• Ebrahim Bagheri, Ryerson University
• Zeinab Noorian, Ryerson University
• Faezeh Ensan, Ferdowsi University of Mashhad

Ebrahim Bagheri awarded Ontario’s Engineering Medal

Dr. Ebrahim Bagheri has received Ontario Professional Engineers, Engineering Medal – Young Engineer (2016), for exceptional achievements in career, community and professional participation before the age of 35. (www.ospe.on.ca)

LS3 director awarded a Tier 2 Canada Research Chair (CRC) in Software and Semantic Computing

Ebrahim Bagheri awarded a Tier 2 Canada Research Chair (CRC) in Software and Semantic Computing.

Dr. Bagheri is developing data analytics software methodologies and platforms that will help to understand and analyze the large volume of user-generated data created in recent years. Through his research, he wants to give software engineers and data scientists the tools and techniques they need to create software applications that can handle the demands of this growing industry.
Announcement Link: http://www.ee.ryerson.ca/news/#BAGHERICRC

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

http://ls3.rnet.ryerson.ca/

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 (bagheri@ryerson.ca).

Interested candidates should submit an application cover letter, a résumé, three relevant publications and contact information of three reference persons to Dr. Ebrahim Bagheri (bagheri@ryerson.ca). 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 
(http://www.journals.elsevier.com/information-and-software-technology/)

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

Submission:
Authors will need to submit their manuscripts through the online submission and editorial system for Information and Software Technology Journal accessible at http://ees.elsevier.com/infsof/default.asp. 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: http://www.ee.ryerson.ca/graduate/symposium/


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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: https://www.ibm.com/ibm/cas/cascon/


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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)