Smart city services are implemented using various data collected from houses and infrastructure within a city. As the volume and variety of the smart city data becomes huge, individual services have suffered from expensive computation effort and large processing time. In order to reduce the effort and time, this paper proposes a concept of Materialized View as a Service (MVaaS). Using the MVaaS, every application can easily and dynamically construct its own materialized view, in which the raw data is converted and stored in a convenient format with appropriate granularity. Thus, once the view is constructed, the application can quickly access necessary data. In this paper, we design a framework of MVaaS specifically for large-scale house log, managed in a smart-city data platform. In the framework, each application first specifies how the raw data should be filtered, grouped and aggregated. For a given data specification, MVaaS dynamically constructs a MapReduce batch program that converts the raw data into a desired view. The batch is then executed on Hadoop, and the resultant view is stored in HBase. We present case studies using house log in a real home network system. We also conduct an experimental evaluation to compare the response time between cases with and without MVaaS.

Materialized views, which are derived from base relations and stored in the database, offer opportunities for significant performance gain in query evaluation by providing quick access to the pre-computed data. A materialized view can be utilized in evaluating a query if it has pre-computed result of some part of the query plan. Although many approaches to utilizing materialized views in evaluating a query have been proposed, there exist several restrictions in selecting such views. This paper proposes new ways of utilizing materialized views in answering an aggregate query. Views including relations that are not referred to in the given query are utilized. Attributes missing from a view can be recovered under certain conditions. We identify the conditions where a view may be used in evaluating a query and present the algorithm to search for the most efficient query among the equivalent ones. We also report on a simulation based on the TPC-H and GRID databases. Simulation results show that our approach provides impressive performance improvements to the data warehousing environment where aggregate views are often pre-computed and materialized.