Selection and Recommendation of Data Analytic Services in the Cloud

云端数据分析服务的选择和推荐

• 批准号: RGPIN-2015-05555
• 负责人: Ding, Chen(Cherie)
• 金额: $ 1.31万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=676938
• 关键词: Selection; Recommendation; Data; Analytic; Services

Big Data is an emerging trend and phenomenon. With more and more data becoming available from various sources, there is an increasing demand on analytic services for the purpose of understanding the data in a better way. Cloud Computing provides a platform to provision these analytic services. In this proposal, we would work on the selection and recommendation of data analytic services in the cloud. Compared to the traditional web service selection, there are several unique challenges for cloud-based analytic services which have not been sufficiently studied and which we address in this proposal; the challenges include the data-directed selection process for analytic services, the identification of meta-features appropriate for Big Data and feasible for measurement, the pairing of analytic services with other supporting cloud services (e.g., infrastructure, storage, data) to offer an end-to-end solution to users, and the ranking of the solutions. Our objective is to design and implement a data analytic service selection and recommendation system in which analytic service is selected based on the given dataset and the historical QoS values of different services on different datasets. A composite service combining analytic software service with other required component services is then recommended based on the predicted end-to-end QoS values. To begin with, we will select a problem domain such as social network analysis. For this chosen domain, we identify the meta-features of its datasets, proper data sources, and representative analytic algorithms and software services, and then we will study the problem of algorithm selection for this type of Big Data. In the next step, we will work on the service vertical composition and end-to-end QoS prediction for composite services. Finally we will build a cloud service marketplace with selection and recommendation components and evaluate our system. The key features of this proposal are as follow: 1) with the proposed selection system, users will be able to select an analytic service, which is best for the given dataset as well as the application domain, and compose a complete solution with recommended supporting cloud services; 2) we will train the HQP to gain a deep understanding on topics such as service selection and recommendation, algorithm selection and meta-learning, analytic service for Big Data, QoS prediction, and service vertical composition; in addition, they will gain hands-on experience working on system building and systematic experimental methods. Our research agenda also fits in perfectly with Canada's Open Government initiative. Since many datasets have been made available online through this initiative, our proposed system can help ordinary citizens select the appropriate analytic services in order to create value and have a better understanding of the otherwise hardly comprehensible raw data.

大数据是一种新兴的趋势和现象。随着越来越多的数据从各种来源变得可用，为了更好地理解数据，对分析服务的需求越来越大。云计算提供了一个平台来提供这些分析服务。在本提案中，我们将致力于选择和推荐云中的数据分析服务。与传统的Web服务选择相比，基于云的分析服务存在几个独特的挑战，这些挑战尚未得到充分研究，我们在本提案中解决了这些挑战;这些挑战包括分析服务的数据导向选择过程，识别适用于大数据并可用于测量的元特征，分析服务与其他支持云服务的配对（例如，基础设施、存储、数据），为用户提供端到端解决方案，以及解决方案的排名。我们的目标是设计和实现一个数据分析服务选择和推荐系统，在该系统中，分析服务的选择基于给定的数据集和不同的服务在不同的数据集上的历史QoS值。然后基于预测的端到端QoS值推荐将分析软件服务与其他所需组件服务相结合的组合服务。开始，我们将选择一个问题域，如社会网络分析。对于这个领域，我们确定了其数据集的元特征，适当的数据源，以及代表性的分析算法和软件服务，然后我们将研究这种类型的大数据的算法选择问题。在下一步中，我们将致力于服务垂直组合和组合服务的端到端QoS预测。最后，我们将建立一个云服务市场的选择和推荐组件，并评估我们的系统。该方案的主要特点如下：1）通过所提出的选择系统，用户将能够选择最适合给定数据集以及应用领域的分析服务，并与推荐的支持云服务组成完整的解决方案; 2）我们将培训HQP，以深入了解服务选择和推荐，算法选择和元学习等主题，大数据分析服务、QoS预测和服务垂直组合;此外，他们将获得系统构建和系统实验方法的实践经验。我们的研究议程也完全符合加拿大的开放政府倡议。由于许多数据集已经通过这一举措在线提供，我们提出的系统可以帮助普通公民选择适当的分析服务，以创造价值，并更好地了解否则难以理解的原始数据。