KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Kee, Kerk F.

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Leveraging Race Prediction Algorithms to Enhance Team Composition in Big Data Science Teams
    (2024-01-01)
    Chumthong, Thanathip
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Kraishan, Obada
    ;
    Kee, Kerk F.
    ;
    Narabin, Akan
    As big data science projects scale in complexity, optimizing team composition has become vital for improving creativity, productivity, and project success. We explore the possibility of incorporating race prediction algorithms for enhancing racial diversity in team composition in big data science projects. This paper evaluates five race prediction algorithms - wru, ethnicolr, ethnicolr2, pyethnicity, and rethnicity - and then discuss their potential in supporting racially diverse team assembly in big data projects. Utilizing three datasets, we assess algorithm performance and applicability, emphasizing their role in building balanced teams that enhance agility, inclusivity, and bias mitigation. We present an actionable methodology for integrating demographic insights into team management. In addition, we propose ethical safeguards to ensure responsible race prediction use, recommending data privacy measures, aggregate-only data handling, and transparency in communication. We argue that when used within ethical constraints, race prediction can support robust team processes, reduce reliance on less diverse teams, and ultimately facilitate more creative and equitable big data project outcomes.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    If you build it, promote it, and they trust you, then they will come: Diffusion strategies for science gateways and cyberinfrastructure adoption to harness big data in the science, technology, engineering, and mathematics (STEM) community
    (2021-10-10)
    Kee, Kerk F.
    ;
    Le, Bethanie
    ;
    Jitkajornwanich, Kulsawasd
    In the big data era, for science gateways (SG) and cyberinfrastructure (CI) projects to have the greatest impacts, they need to be widely adopted in the scientific community. However, diffusion activities, or activities aimed to spread SG/CI in the science, technology, engineering, and mathematics community, are often an afterthought in projects. We warn against the fallacy of “If You Build It, They Will Come.” Projects could be intentional in promoting tool adoption. Based on an analysis of 83 interviews with 66 administrators, developers, scientists/users, and outreach educators of SG/CI, we identified seven external communication practices—raising awareness, personalizing demonstrations, providing online and offline training, networking with the community, building relationships with trust, stimulating word-of-mouth persuasion, and keeping reliable documentation. With these strategies, we revised the pop culture line to “If You Build It, Promote It, and They Trust You, Then They Will Come.” We also observed the beliefs that external communication is mainly necessary when seeking continuous funding, and it belongs to the skillset of nontechnical staff. These two beliefs may explain why external communication is underemphasized in many SG/CI projects. The article serves as evidence to justify a bigger budget in funding proposals for diffusion strategies to increase adoption and broader impacts.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    The Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data
    (2020-01-01)
    Pongto, Ratchanont
    ;
    Wiwattanaphon, Nopparat
    ;
    Lekpong, Peerapon
    ;
    Lawawirojwong, Siam
    ;
    Srisonphan, Siwapon
    Marine natural disasters have direct impacts on countries as well as their residents living on and near the coast. Warning and monitoring system can aid in reducing the loss of lives in the event of a disaster. HF (high frequency) radar, an IoT-enabled ocean surface current monitoring system, implementation is one of the first attempts towards achieving this goal. Although HF systems can monitor sea current patterns in terms of speed and direction for each of the pixels of the coverage area, it fails to predict future values, which are essential to many applications such as oil-spill trajectory prediction (using the GNOME suite: General NOAA Operational Modeling Environment), water quality control and management, and optimized sea navigation. In this paper, we propose a model, called the grid-based spatial ARIMA (auto-regressive integrated moving average), to estimate the forecast values. As a result, the full potential of the HF systems can be utilized. The method considers not only observations of POI (point of interest), but also its neighboring pixels when predicting future values. The proposed method is implemented and compared with other existing approaches, including baseline, kNN, traditional ARIMA model, and LSTM (long short-term memory) techniques. The experimental results showed that our approach outperformed other methods in V comp prediction (with RMSEs of 6.23265) with a configuration of (2, 0, 1) as (p, d, q) and a historical dataset of 1 day and 7 h prior. This configuration was found to be the best combination.