Louai Alarabi is currently assistant professor at UMM-ALQURA University in the department of computer Science. He graduatede from the Computer Science and Engineering Department at the University of Minnesota Twin Cities. He is a member of the Data Management Lab supervised by Prof. Mohamed Mokbel. He received his B.SC at Umm Al-Qura University in Computer Science. His research interest lies in the board area of databases with the focus on big data management and spatio-temporal computing. He is the inventor of ST-Hadoop, a comprehensive distributed spatio-temporal data management system. He also invented TAREEG, a distributed MapReduce framework for extracting a spatial feature from the map. Louai enjoys building systems; he developed several other systems including MNTG, SHAREK , and TAGHREED. His research recognized by the first place and a gold medal award in student research competition at ACM SIGSPATIAL/GIS 2018, among best paper award at SSTD 2017, Finalist of student research competition at ACM SIGMOD 2017, and best demonstration award at U-Spatial Symposium 2014. His research funded by a collaboration with UMM-AlQURA Universtiy, KACST GIS Technology Innovation Center, and University of Minnesota. Louai worked as software engineer for two years in industry at Advanced Electronic Company Ltd. Louai also worked as research assistant at the KACST GIS Innovation Center for seven months. Louai has a teaching experience, as he worked as teaching assistant at both UMM-AlQURA Universtiy and the University of Minnesota.
Responsibilities included : Preparing and discussing homework exercises and programming assignments, delivering lab tutorials and recitations, and grading exams and quizzes.
Courses: CSCI-4061:Operating System, and CSCI-4707:Practice of Database Systems
Member of the Data Management group doing research in microblog data management and big spatio-temporal data processing, building these systems as a proof of concept using and extending SpatialHadoop to support processing and indexing the temporal dimension, document the outputs as research papers, and giving demonstrations for business purposes.
Responsibilities included : Preparing and discussing homework exercises and programming assignments, delivering lab tutorials and recitations, and grading exams and quizzes, finally publishing a free online tutorial of Clips an artificial intelligent language for students.
Courses: Computer Graphics, Advanced Programming Languages, Structured Programming Languages, Expert System , and Software Engineering.
Member of a research and development team mainly working on implementing a DLMS protocol on ARM Processor. Also, developed Electronic Gateway desktop application called Parameterization Software PS, where it has designed and developed as a second generation of Digital Meters for Saudi Electrical Company under the Authority of AEC.
Workshop & Abstract
ST-Hadoop is a MapReduce framework that acknowledges the fact that space and time play a crucial role in query processing. ST-Hadoop is an open-source extension of a Hadoop framework that injects the spatiotemporal awareness in the code base of four layers inside SpatialHadoop, namely, language, indexing, MapReduce, and operations layers. The spatio-temporal indexing techniques inside ST-Hadoop primarily tuned to provide the accommodation of new updated dataset efficiently without the need to rebuild its index. The key point behind the performance gain of ST-Hadoop is the idea of indexing, where data are temporary loaded and divided across computation nodes. For more information, please visit: http://st-hadoop.cs.umn.edu.
TAREEG is a MapReduce-Based System for Extracting Spatial Data from OpenStreetMap Real spatial data, e.g., detailed road networks, rivers, buildings, parks, are not really available in most of the world. This hinders the practicality of many research ideas that need a real spatial data for testing experiments. Such data is often available for governmental use, or at major software companies, but it is prohibitively expensive to build or buy for academia or individual researchers. TAREEG; a web-service that makes real spatial data, from anywhere in the world, available at the fingertips of every researcher or individual. TAREEG gets all its data by leveraging the richness of OpenStreetMap dataset; the most comprehensive available spatial data of the world. Yet, it is still challenging to obtain OpenStreetMap data due to the size limitations, special data format, and the noisy nature of spatial data. TAREEG employs MapReduce-based techniques to make it efficient and easy to extract OpenStreetMap data in a standard form with minimal effort. TAREEG is accessible via www.tareeg.org.
MinnesotaTG is a project developed at the University of Minnesota. MinnesotaTG is built based on two existing traffic generators: (1) BerlinMod and (2) Thomas-Brinkhoff. The purpose of MinnesotaTG is to take an arbitrary region in the United States and generate traffic data from that region. Without this tool, generating this traffic is a complicated and drawn out process because of the number of configuration steps necessary to get either Thomas-Brinkhoff or BerlinMod both up and running, and able to work on a user specified region. The generation of the traffic is not done by the tool itself, but rather it is performed by these two different traffic generators. For more information, please visit: http://mntg.cs.umn.edu/.
Taghreed is a system for querying, analyzing, and visualizing geotagged tweets. Taghreed is the first to manage both recent and historical Twitter data. It digests incoming fast data in real time and scale for billions of historical data items. On both, scalable spatio-temporal keyword queries are supported to facilitate efficient and scalable spatio-temporal analysis and visualization of Twitter data. Taghreed is powering two startups serving social media analysis services for Middle Eastern customers at Wadi Makkah innovation incubator. please visit: http://www.gistic.org/taghreednew/.