Selection and Recommendation of Data Analytic Services in the Cloud

大数据是一个新兴的趋势和现象。随着从各种来源获得的数据越来越多，为了更好地理解数据，对分析服务的需求也越来越大。云计算提供了一个平台来提供这些分析服务。在这个提案中，我们将致力于云中数据分析服务的选择和推荐。与传统的Web服务选择相比，基于云的分析服务有几个独特的挑战，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流程、识别适合大数据且可用于测量的元特性、分析服务与其他支持云服务(例如基础设施、存储、数据)的配对以向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，该系统根据给定的数据集和不同数据集上不同服务的历史Qos值来选择分析服务。然后，基于预测的端到端Qos值，推荐将分析软件服务和其他所需组件服务组合在一起的组合服务。首先，我们将选择一个问题领域，例如社交网络分析。对于所选择的领域，我们确定了它的数据集的元特征、合适的数据源以及具有代表性的分析算法和软件服务，然后我们将研究这类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服务质量预测。最后，我们将构建一个包含选择和推荐组件的云服务市场，并对我们的系统进行评估。该方案的主要特点是：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和应用领域的分析服务，并结合推荐的支持云服务组成完整的解决方案；2)培训HQP，使其对服务选择与推荐、算法选择与元学习、大数据分析服务、服务质量预测、服务垂直组合等主题有深入的理解；此外，他们还将获得系统建设和系统性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倡议在网上提供，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，并更好地理解否则难以理解的原始数据。

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.