Nora Yousef Alkhamees

Nora Alkhamees is an assistant professor in the Management Information Systems (MIS) Department, CBA, KSU. She received her PhD in Computer Science from University of Essex, UK. She received her MSc in Information Systems and her BSc in Information Technology from College of Computer and Information Sciences, KSU. Her current research interests include big data and data analytics, data mining, machine learning, and computational finance.
  •  2014-2019: PhD in Computer Science, School of Computer Scieince and Electronic Engineering, University of Essex, UK.
  • 2009-2011: MSc in Information Systems, College of Computer and Information Sciences, King Saud University
  • 2003-2008: BSc in Information Technology, College of Computer and Information Sciences, King Saud University. With second grade honor.


  • Aug 2019- Present Assistant Professor at Department of MIS, CBA, KSU.
  • Oct 2014- July 2019: PhD thesis.

Title:        Developing event identification methods for structured and unstructured data streams.
Abstract: Data, now more than ever before, are continuously being generated in huge volumes, and at rapid speed. Data may originate from various sources, for instance: sensor readings, financial transactions, social networks, etc.. A data stream is a continuous sequence of data arriving in almost real-time and often at a high speed.
In this thesis, we are interested in benefiting from the availability of such data and developing methods for detecting the occurrence of events from data streams, such as a text stream and a price time-series stream. Hence, we have explored event identification from structured and unstructured data streams in the domain of finance.
We employ the Directional Change (DC) approach to high frequency time-series streams to identify significant price transitions (i.e. events). DC is an event-based approach for summarizing price movements based on a fixed, a-priori threshold. We propose a dynamic threshold definition method, which replaces the fixed threshold and is appropriate for markets that operate over specific opening and closing times. A dynamic threshold provides more flexibility and extends the DC approach allowing the identification of price changes in continuously changing environments.
With the proliferation of social media data reporting on all aspects of human activity, being able to automatically identify events is becoming increasingly important. We present a framework for detecting the occurring events on a daily basis, via social network streams. We develop and extend a Frequent Pattern Mining method by proposing a dynamic support definition method to replace the fixed support. As the number of text posts streamed each day is not fixed, a dynamic support, can adapt to the nature of data streams and can improve the identification of events.
Finally, we explore whether we can bring together the insights from the time-series stream and the social network stream to understand if events as identified from both streams can be correlated.

  • Aug 2011- Aug 2019 Lecturer at Department of MIS, CBA, KSU.
  • Sep 2009- June 2010: Master Graduation Project.

Title:         Data Distribution Techniques for Data Warehouse, Data Warehouse Striping.
Abstract: Data warehouses are mainly used to store large amounts of data. This data is often used for On-Line Analytical Processing (OLAP) where short response time is essential to support on-line decision support. As a consequent to these needs the Data Warehouse Striping (DWS) was invented. The DWS technique is a data partitioning approach especially designed for distributed data warehousing. In DWS the fact table is distributed by an arbitrary number of low-cost computers and the queries are executed in parallel by all the computers, guarantying a nearly optimal speed up and scale up. The research that was made contained a detailed study and an evaluation for Data Warehouse Striping technique. Also, it is compromised of an experiment that deploys the DWS technique using three computer nodes which were connected through a fast Ethernet network, queries were run in parallel among all computers. Our findings regarding the query processing time was optimizing an almost optimal speedup.

  • July 2008- Aug 2011 Teaching Assistant (TA) Department of MIS, CBA,  KSU.
  • Sep 2007-June 2008: Bachelor Graduation Project

Title:            Email Classification System (e-classifier)
Summary:  e-classifier is an outlook add-in classification system that classifies emails into classes defined by the user. Initially the user will move the emails to suitable classes. At this point the system observes the user behavior and learns how to classify the emails accordingly. Then the system will make this classification automatically

  •   June - Aug 2007:Participating in the summer training program in Alrajhi Bank.


Conferences, workshops, and summer schools

  • Participated in the IEEE Big Data conference in 2016, 2017, and 2019.
  • Participated in the BLG DRC Conference in 2018.
  • Organized the 10th Computer Science and Electronic Engineering Conference (CEEC 18) Conference in 2018 (publicity and public relations chair).
  • Participated in the DSAA conference in 2017.
  • Organized the IADS Analytics and Data Science Summer School in 2017, 2018, and 2019.
  • Participated in the Computational Finance workshop in University of Essex in 2016 and 2017 (won the best student presentation for both years).
  • Attended the Big Data and Analytics Summer School organized by University of Essex in 2015 and 2016.
  • Aug 2008: Presented a paper entitled “e-Classifier A Bi-lingual classification system” in the ITsim’08 conference in Malaysia.
  • March 2009: Attended the First International Conference in e-Learning & Distance Learning in Riyadh. 


  1. N. Alkhamees and M. Fasli, “The Dynamic-FPM: An Approach for Identifying Events from Social Networks Using Frequent Pattern Mining and Dynamic Support Values,” in the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles.
  2. N. Alkhamees and M. Fasli, “Event detection from time-series streams using directional change and dynamic thresholds," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 1882-1891. doi: 10.1109/Big-Data.2017.8258133
  3. N. Alkhamees and M. Fasli, “An exploration of the directional change based trading strategy with dynamic thresholds on variable frequency data streams," 2017 International Conference on the Frontiers and Advances in Data Science (FADS), Xi'an, 2017, pp. 108-113. doi: 10.1109/FADS.2017.8253207
  4.  N. Alkhamees and M. Fasli, “A Directional Change Based Trading Strategy with Dynamic Thresholds," 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, 2017, pp. 283-292. doi: 10.1109/DSAA.2017.48
  5. N. Alkhamees and M. Fasli, “Event detection from social network streams using frequent pattern mining with dynamic support values," 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, 2016, pp. 1670-1679. doi:10.1109/BigData.2016.7840781

Research Intrests:

  • ​Big data, Data analytics, Data science
  • Data mining, Infromation retrieval
  • Machine learning
  • Stream reasoning
  • Patterns recognition
  • Database, Data Warehouse
  • Computational finance, Trading stratigies


  • MIS 201 (Managemant of Infromation Systems
  • MIS 214 (Database)
  • MIS 321 (Advanced Database)
  • MIS 350 (Dicision Support Systems and Expert Systems)
  • MIS 419 (Knoeledge Management and Data Mining)
  • MIS 477 (COOP)