In recent years, time series data are everywhere across different industry, which creates a huge demand on time series data analysis, such as pattern search. Meanwhile, it is increasingly realized that only when pattern search results together with information from relational tables could be used in a programming-free way, can they perform analysis on time series conveniently. Hence, casual users highly demand that queries involving pattern search could be performed via SQLs. However, existing database products supporting time series data type lack the capability to perform pattern searches on time series data. This paper presents SearchonTS, an extendable framework for in-database pattern search on time series data. It provides a series of interfaces so that time series index and pattern search can be added and performed in a uniformed and query optimized manner. SearchonTS is implemented as an extension on Informix, which is a database product in IBM software product series. It targets a future release of IBM Informix. We have implemented index-based pattern search for Euclid Distance(ED) via SearhonTS to demonstrate its usability for developers. And real scenario is also provided to show SQL involving pattern search so that users can have a more clear experience of the convenience.

In recent years, the evolving of IoT (Internet of Things) has resulted in the deployment of massive numbers of sensors in various fields, such as the Energy and Utility (E&U) industry. These sensors are continuously producing a huge amount of time series data, which creates a correspondingly huge demand for time series data analysis, such as pattern searching. However, analysis on time series data is currently implemented as custom applications, a strategy that suffers from low efficiency and high maintenance costs. Hence there is a need to provide a service for analysis on time series data that reduces maintenance costs and enhances query efficiency. Existing time series data management services lack the capability to perform pattern searches on the massive amount of data from sensors. This paper presents Time Series analytics as a Service (TSaaaS), a scalable analytic service for time series data in IoT scenarios. We designed pattern searching in TSaaaS that can support efficient and effective searching on truly massive amounts of time series data with very little overhead on the IoT system. To simplify access to the TSaaaS, we created a group of RESTful web interfaces. TSaaaS is implemented as an extension to the Time Series Database service in the IBM cloud platform offering (Codename: BlueMix), which is a new product to accelerate IoT application development. TSaaaS targets a future release of the Time Series Database service. We have conducted proofs of concept (PoC) of TSaaaS with real-world customers from power meter management and bridge monitoring in China. The pilot results and other experiments show that for a selection of pattern cases provided by customers, pattern searches via our service are 10-100 times faster than the existing techniques, while the additional storage cost for the service provider accounts for only about 0.4% of original time series data.

In the era of the Internet of Things (IoT), increasing numbers of applications face the challenge of using current data management systems to manage the massive volume of operational data gener-ated by sensors and devices. Databases based on time series data model, like PI Server, are developed to handle such data with operational technology (OT) characteristics (high volume, long lifecycle, and simple format). However, while achieving excellent write performance, these database systems provide limited query capabilities. In this paper, we present the next-generation Opera-tional Data Historian (ODH) system that is based on the IBM© Informix© system architecture. The system combines the write advantages of the existing time series databases and the ability to run complex queries in SQL. We demonstrate the high efficiency of our system for both writing and querying data with a variety of case studies in the industries of Energy and Utilities and con-nected vehicles. In addition, we present the first benchmark, IoT-X, to evaluate technologies on operational data management for IoT.