abdussalam alawini
Teaching Assistant Professor at University of Illinois at Urbana-Champaign
teaching assistant professor - university illinois urbana-champaign
University of Illinois at Urbana-Champaign
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Urbana-Champaign, Illinois Area
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Teaching Assistant Professor
University of Pennsylvania
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Greater Philadelphia Area
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Postdoctoral Researcher
Portland State University
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Portland, Oregon Area
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Adjunct Instructor
Portland State University
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Portland, Oregon Area
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Research Assistant
Portland State University
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Coordinator, Office of Information Technology
Judi for Food Industries
Tripoli, Libya
IT Manager and SW Architect
University of Tripoli
https://www.linkedin.com/company/university-of-tripoli
Tripoli, Libya
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Teaching Assistant
African Bottling Operation (Pepsi)
https://www.linkedin.com/company/african-bottling-operation-pepsi-
Tripoli, Libya
Database Administrator and Internal Systems Auditor
United Nations Children Fund (UNICEF)
Tripoli, Libya
Software Engineer Intern
Doctor of Philosophy (PhD)
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Portland State University
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MS
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Portland State University
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MS
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Portland State University
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Bachelor of Science (B.S.)
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University of Tripoli
https://www.linkedin.com/school/university-of-tripoli/
ACM Special Interest Group on Management of Data (SIGMOD)
Member
An increasing amount of information is being published in structured databases and retrieved using queries, raising the question of how query results should be cited. Since there are a large number of possible queries over a database, one strategy is to specify citations to a small set of frequent queries - citation views - and use these to construct citations to other "general" queries. We present three approaches to implementing citation views and describe alternative policies for the joint, alternate and aggregated use of citation views. Extensive experiments using both synthetic and realistic citation views and queries show the trade-offs between the approaches in terms of the time to generate citations, as well as the size of the resulting citation. They also show that the choice of policy has a huge effect both on performance and size, leading to useful guidelines for what policies to use and how to specify citation views.
Data Citation: Giving Credit Where Credit is Due
SIGMOD
http://www.cis.upenn.edu/~susan/cis700/Papers/DataCitation.pdf
In today’s era of big data-driven science, an increasing amount of information is being published as curated online databases and retrieved by queries, raising the question of how query results should be cited. Because it is infeasible to associate citation information with every possible query, one approach is to specify citations for a small set of frequent queries–citation views–and then use these views to construct a citation for general queries. In this paper, we describe this model of citation views, how they are used to construct citations for general queries, and an efficient approach to implementing this model. We also show the connection between data citation and data provenance.
Data Citation: A New Provenance Challenge.
IEEE Data Eng. Bull. 41 (1), 27-38
http://www.dei.unipd.it/~silvello/papers/2018_IEEE_ADSTW.pdf
Data provenance approaches track how the answer to a database query derive from input items; however, prior approaches used “positive” provenance and were not directly usable for explaining “expected” but missing answers. A similar problem arises with the failure of integrity constraints. Our perspective is to offer explanations via possible (minimal) repairs using provenance. This is useful for debugging, repairing, and cleaning databases. In this paper, we introduce a novel approach to this problem for both missing/erroneous answers and integrity failures. The approach uses recent advances in provenance for first-order model checking.
Provenance Analysis for Missing Answers and Integrity Repairs
IEEE Data Eng. Bull.
https://www.researchgate.net/profile/Abdussalam_Alawini/publication/325090722_Provenance_Analysis_for_Missing_Answers_and_Integrity_Repairs/links/5af5aaec0f7e9b026bceafec/Provenance-Analysis-for-Missing-Answers-and-Integrity-Repairs.pdf
Automating Data Citation in CiteDB
The 43rd International Conference on Very Large Databases
https://www.dropbox.com/s/rvqe3iax7f8d5v3/p996-alawini.pdf?dl=0
Automating data citation: the eagle-i experience
The Joint Conference on Digital Libraries 2017
http://www.dei.unipd.it/~silvello/papers/2017-JCDL-ACDS.pdf
Identifying Relationships between Scientific Datasets
PhD Dissertation
http://pdxscholar.library.pdx.edu/open_access_etds/2922
Before scientists can analyze, publish, or share their data, they often need to determine how their datasets are related. Determining relationships helps scientists identify the most complete version of a dataset, detect versions of datasets that complement each other, and determine multiple datasets that overlap. In previous work, we showed how observable relationships between two datasets help scientists recall their original derivation connection. While that work helped with identifying relationships between two datasets, it is infeasible for scientists to use it for finding relationships between all possible pairs in a large collection of datasets.  In order to deal with larger numbers of datasets, we are extending our methodology with a relationship-prediction system, ReDiscover, a tool to identify pairs from a collection of datasets that are most likely related and the relationship between them. We report on the initial design of ReDiscover, which uses machine-learning methods such as Conditional Random Fields and Support Vector Machines to the relationship-discovery problem. Our preliminarily evaluation shows that ReDiscover predicted relationships with an average accuracy of 87%.
Toward Automated Prediction of Relationships among Scientific Datasets
June 11, 2015
It seems inevitable that the datasets associated with a research project proliferate over time: collaborators may extend datasets with new measurements and new attributes, new experimental runs result in new files with similar structures, and subsets of data are extracted for independent analysis. As these "residual" datasets begin to accrete over time, scientists can lose track of the derivation history that connects them, complicating data sharing, provenance tracking, and scientific reproducibility. In this paper, focusing on data in spreadsheets, we consider how observable relationships between two datasets can help scientists recall their original derivation connection. For instance, if dataset A is wholly contained in dataset B, B may be a more recent version of A and should be preferred when archiving or publishing.

We articulate a space of relevant relationships, develop a set of algorithms for efficient discovery of these relationships, and organize these algorithms into a new system called ReConnect to assist scientists in relationship discovery. Our evaluation shows that existing approaches that rely on flagging differences between two spreadsheets are impractical for many relationship-discovery tasks, and a user study shows that ReConnect can improve scientists' ability to detect useful relationships and subsequently identify the best dataset for a given task.
Helping Scientists Reconnect their Datasets
Proceeding SSDBM '14 Proceedings of the 26th International Conference on Scientific and Statistical Database Management
http://dl.acm.org/citation.cfm?id=2618263
In this chapter we evaluate the selection of a call tracking feature for an existing marketing automation solution. This type of selection process has become much more complex over time based on the sheer volume of offerings available, different technical approaches to implementation, and service plans (features plus costs). In order to manage this complexity for decision making, we gathered a set of core requirements from the client, assembled a panel of experts to rank the importance of requirements, and then evaluated the potential solutions based on those criteria. The actual decision making methodology used in this study is the hierarchical decision model (HDM) testing two alternative methods for evaluating the expert criteria ranking. In this case, by focusing on client requirements, rather than specific technologies or implementation approaches, allows us to greatly simplify this complex decision making process in the absence of a more detailed technical analysis of every possible solution.
Technology Development (Call Tracking Technology Selection Model)
Innovation, Technology, and Knowledge Management
http://link.springer.com/chapter/10.1007%2F978-3-319-05651-7_2
Includes technology management case studies from national organizations across the USA
Introduces methods and tools to manage transformation in the electric industry
Uses lessons learned from other industries to encourage innovation in the electric industry
Technologies such as renewable energy alternatives including wind, solar and biomass, storage technologies and electric engines are creating a different landscape for the  electricity industry. Using sources and ideas from technologies such as renewable energy alternatives, Research and Technology Management in the Electricity Industry explores a different landscape for this industry and applies it to the electric industry supported by real industry cases.

 

Divided into three sections, Research and Technology Management in the Electricity Industry introduces a range of  methods and tools including technology assessment, forecasting, roadmapping, research and development portfolio management and technology transfer. These tools are the applied to emerging technologies in this industry with case studies including data from various organizations including Bonneville Power Administration and Energy Trust of Oregon, from sectors including lighting and wind energy. The final section considers innovation through these technologies.

 

A product result of a collaboration between Bonneville Power Administration and Portland State University, Research and Technology Management in the Electricity Industry is a comprehensive collection of methods, tools, examples and pathways for future innovation in the electricity industry.
Research and Technology Management in the Electricity Industry (Technology Adoption: Building IT)
Springer-Verlag London 2013
http://www.springer.com/energy/systems,+storage+and+harvesting/book/978-1-4471-5096-1
President's award for university service at the doctoral level.
Portland State University
President's Award for University Service
Based on Mr. Alawini's Portland State University academic achievements and nomination, he has been accepted to the university HonorSociety.org member society.
HonorSociety.org
International Academic Achievement Award
Mr. Alawini is placed in the top 15 percent of his class! The recognition he deserves for his hard-earned accomplishments has arrived.  For this, the Golden Key International Honor Society chapter at Portland State University accepted him to become a member of our prestigious Society.
Golden Key International Honor Society
International Stduent Academic Stand Out
Awarded a scholarship to study graduate school in the U.S.
University of Tripoli
University of Tripoli Study Abroad Scholarship
Advanced C Programming
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Algorithms
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Computer Architecture
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Introduction to C Language
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Statistics 1
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Statistics 2
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Cloud Data Management
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Data Stream Systems
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Machine Learning Practicum 1,2 and 3
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Advanced Engineering Economics
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Building Software Systems with Components
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Data Manipulation using XML
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Database Implementation
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Decision Making
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Functional Programming Using Haskell
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Human/Computer Interaction
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Introduction to Databases
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New Venture Management
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Open Source Software Development
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Operating Systems
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Operation Research
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Project Management
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Research Project: Cloud-based Personal Medical Records System
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Research Project: Clustering XML Documents (Drug info,) based on their structure.
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Software Engineering
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Technology Marketing
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ReDiscover is a tool to identify pairs from a collection of datasets that are most likely related and the relationship between them. ReDiscover applies a conditional probabilistic model, namely Conditional Random Fields (CRFs) to extract data columns. It also uses binary classifiers based on support vector machines (SVMs) to automate the column-matching process, and to predict relationships between pairs of datasets.
ReDiscover: Automated Prediction of Relationships among Scientific Datasets
The goal of this project is to develop tools and techniques for automating data citation. We aim to build a citation framework that can work across a variety of different types of databases, (e.g., relational, graph or XML).
CiteDB: Data Citation
https://www.researchgate.net/project/CiteDB
Scientific datasets associated with a research project proliferate over time as a result of activities, such as sharing datasets among collaborators, extending existing datasets with new measurements, and extracting subsets of data for analysis. As such datasets begin to accumulate, it becomes increasingly difficult for a scientist to keep track of their derivation history, which complicates data sharing, provenance tracking, and scientific reproducibility. Understanding
what relationships exist between datasets can help scientists recall their original derivation connection. For instance, if dataset A is contained in dataset B, then the connection could be that A was extended to create B.

We propose the relationship-identification methodology for testing relationships between pairs of datasets, develop a set of algorithms for efficient discovery of these relationships, and organize these algorithms into a new system called ReConnect to assist scientists in relationship discovery.
ReConnect: a tool for detecting relationships between scientific spreadsheets
Environment
Hands On Greater Portland
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Volunteer
University of Illinois Urbana-Champaign
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36297480
Illinois Summer Teaching Institute 2021
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Coursera
drive.google.com
Programming Mobile Applications for Android Handheld Systems
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Waukegan, IL
Cesar Monsalud
CS @ University of Illinois Urbana-Champaign
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Urbana-Champaign Area
Wade Fagen-Ulmschneider
Teaching Assoc. Professor, Computer Science, University of Illinois 🧡💙
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Urbana, IL
Margaret Fleck
Research Professor at University of Illinois at Urbana-Champaign
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Champaign, IL
Shichen Li
PhD student @UIUC | Machine learning and Data analysis in Manufacturing | Incoming 2022 summer SWE-ML (PhD) intern@Meta
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Urbana-Champaign Area
Suryasri Kalidas
University of Illinois at Urbana-Champaign | vGHC'21| Looking for Full Time
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Champaign, IL
Franco Cubas
M.S. student Aerospace Engineering with an interest in aerodynamics and structures
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Champaign, IL
Mohammad Zawaadul Karim
CS + Business @ILLINOIS | Co-Founder/ Data Scientist at AID COVID-19 | Consultant at Illinois Business Consulting |  Undergraduate Research Assistant
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United States
Yujia Yan
Software Engineer @ Oracle | MCS @ UIUC
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Menlo Park, CA
Askar Mukhanov
Data Scientist at Meta
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Champaign, IL
Adarsh Suresh
CS @ Illinois | SWE Intern @ Google | Intro to CS II Lead Course Assistant | Incoming SWE Intern @ IMC Trading
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Greater Boston
Rachael Wei
CS@Illinois | Incoming Software Engineering Intern at MongoDB
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Urbana-Champaign Area
Aditya Dutt
Incoming Robotics Engineer Intern at Amazon Robotics
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Urbana, IL
Asher Mai
ECE 220 and 391 Course Staff | Computer Engineering UIUC ‘23
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Greater Boston
Henry Crawford-Eng
ECE 391 Course Staff
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United States
Ananya Subramanian
Math major and CS minor @ UIUC | Incoming Intern at AMD
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Champaign, IL
JiHo Han
Master of Computer Science Candidate at University at Albany, SUNY
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Urbana-Champaign Area
Matt Geimer
SWE Intern @ Apple | CS+Economics @ UIUC
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Greater Charlottesville Area
David Nemer
Assistant Professor at University of Virginia
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Riverside, CA
Evangelos (Vagelis) Papalexakis
Associate Professor at University of California, Riverside
Hisham Benotman
      
          



            
              
        
              
  

    
        Abdussalam is a long term colleague of mine. We worked together as teaching assistants at the University of Tripoli (previously Alfateh) in Libya and we came together to pursue an MSc and then a PhD in the US. As a very skilled and dependable person, Abdussalam was my favorite partner in my graduate school projects. In software projects, research papers, and class presentations, Abdussalam always finished his task on time and with high quality. We worked together as student employees in the OIT department at Portland State University; I have seen how Abdussalam managed the stress of pursuing two master degrees and working as a supervisor for the largest two computer labs at PSU. Abdussalam is proactive and a team leader; He has not only showed that in school and work, but also in student-community service at PSU and Portland. He deservedly received the PSU dean’s award for university service. Nasser Gabaj
      
          



            
              
        
              
  

    
        I was impressed with Abdussalam's enthusiasm, communication skills, reliability, dedication and professional demeanor when I hired him 12 years ago as a Database Administrator and Internal Systems Auditor within ABO (African Bottling Operations "Pepsi Bottler"). During the three years that was there Abdussalam reported to me, he consistently demonstrated all of these qualities and more, and I heartily endorse him for any Database Administrator position.

If you have any questions, please let me know,

Nasser
Intertech Software Developer Network | .NET, Java, and Cloud Computing
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Google Cloud Platform
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Oregon Career Development Association (OCDA)
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Portland State University
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Portland Oregon Jobs
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Portland Web Developers of Oregon
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Data Science Community (moderated)
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Gulf Recruitment
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Intel Student Lounge
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Open Source Bridge
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PDX Technology Professionals - Portland Oregon
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Data Science & Machine Learning
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Al Fateh University Alumni
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Amazon AWS Architects
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