Real-time gross settlement system (RTGS) is the cornerstone of inter-bank payment business. Spectacular expansion of large-value wholesale payment has forced financial institutions to implement inter-bank payment systems (IBPS) with higher level of throughput, security, and stability. This promising approach to orchestrating IBPS utilizes emerging blockchain technology, which has been successfully harnessed to deliver distributed trust and confidentiality for diverse financial service applications. However, blockchain is not a silver bullet for IBPS, which faces multiple challenges incurred by high value transactions. Financial institutions expect not only a simple migration from traditional RTGS to a blockchain platform, but a decentralized system with better confidentiality, instruction settlement finality, liquidity saving mechanism, and more efficient methods of gridlock resolution. In this paper, we introduce an end-to-end IBPS prototype based on Hyperledger Fabric enterprise blockchain platform. The prototype supports gross settlement, gridlock resolution, and reconciliation for inter-bank payment business. As a preliminary research of Ubin Project, this prototype is proved to provide higher level of payment settlement service.

In the Internet of Things (IoT) era, with ubiquitous remote sensing devices and other diverse data sources, nearly everything can forward voluminous data continuously, in real-time, which drives demand to perform real-time analytics on uninterrupted IoT data flows. The typical resultant approach is a cloud-centered architecture providing an analytic service for real-time IoT data processing. However, a cloud-centered IoT analytic service cannot guarantee real-time responsiveness has a high-fee pay-as-you-go business model, and opens data privacy concerns. Hence, it becomes rational to shift analytic workloads to the edge and provide a management service for edge analysis. Existing work on providing edge analytics as a service encountered challenges such as lacking a lightweight way to compose IoT applications based on multiple service providers, lacking a flexible and unified way to define domain-specific analytic logic, and maintaining efficiency when processing data on a resource-limited edge. This paper presents EAaaS, a scalable analytic service for enabling real-time edge analytics in IoT scenarios. In this work, we propose a unified rule-based analytic model to ease user's programming efforts in specifying rule-based analytic logic. Moreover, we also designed and implemented a high performance edge engine to apply rule-based analytic on incoming device data streams. To simplify the access to EAaaS service, a group of RESTful web interfaces is also designed for edge analytic management on cloud and flexible composition with external services. EAaaS is implemented as a part of IBM Watson IoT Platform, which is a cloud service for elementary IoT application development on IBM Bluemix cloud announced by IBM recently. We have conducted proof of correctness (PoC) of EAaaS with customers from boat racing in the U.S. and collected valuable feedback from customers for further enhancement of EAaaS's flexibility and usability.

In the Internet of Things (IoT) era, an increasing number of data management applications, such as for connected vehicles and smarter cities, face the challenge of querying and analyzing massive volumes of spatiotemporal data. These applications frequently perform queries that join moving objects with spatial data, such as selecting sub-tracks crossing a bridge. However, spatiotemporal queries are not well supported or natively supported by current state-of-the-art relational database systems. Most of existing systems build a spatial index directly over the raw spatiotemporal data, which leads to performance issues when scaling out for both indexing and query. In this paper, we focus on building a Spatio Temporal historian as a Service (STaaS) by extending the IBM Blue mix Time Series Database service. The STaaS service manages to process spatiotemporal queries over high volume historical data. The experiments show that STaaS service could easily scale out by adding shards, and achieve dramatic speed-up on spatiotemporal query with support of our hybrid data store. Moreover, we have already deployed STaaS on Blue mix Staging (Internal User Testing) Zone to collect feedback for improvement before porting it into the product zone in the future.

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.

Test coverage is popularly applied in software companies to measure test efforts and create regression test suite. Many service providers actively provide test as a service to software companies to reduce the cost of software test. However, it is a big challenge to store increasing massive test coverage data, such as software build, test traces, etc. In addition, it is also a data-intensive processing for coverage statistics summarization and regression test generation. Hence, data availability and scalability becomes two obstacles for the traditional solutions to be deployed as a service. To overcome these obstacles, in this paper, we propose to leverage the cloud techniques including BigTable and MapReduce to deploy test coverage service.