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.

Nowadays vast amounts of data are being produced in continuous ways. They may come from sensors, smart meters, application logs, monitoring software etc. The data need to be processed in realtime to gain actionable insights. Services like smart grid load balancing, cloud platform maintenance, can be carried out in an efficient way. Stream processing is the programming paradigm that answers such demand. When talking about stream processing, we can easily recall several famous open-source software frameworks such as Spark Streaming, Samza, Flink and Storm. Although they provide distributed, robust, low-latency stream processing engines, it's still difficult for an end user to set up a usable stream processing application from scratch. Firstly, users are required to write code to define their business related stream processing logic. Secondly, the submission and update of the stream processing logic require service restart, therefore it may lead to service unavailability for minutes. Thirdly, extra operation effort are required for handling scaling and failover issues. In this paper, we present RTA, a released research service on realtime data processing. The RTA service fills the gap between the stream processing requester and the existing software stacks. It offers a SQL-like stream query language for defining stream processing logic definition over streaming data. It allows users easily define their stream processing logic without programming. In RTA service, stream processing logic is also treated as a type of input, which enables online logic update without service downtime. The RTA service also provides scalability, high availability and resource isolation for serving multiple tenants. In this paper, we also provide a comprehensive evaluation of our service through a case study.

MapReduce and Spark are two very popular open source cluster computing frameworks for large scale data analytics. These frameworks hide the complexity of task parallelism and fault-tolerance, by exposing a simple programming API to users. In this paper, we evaluate the major architectural components in MapReduce and Spark frameworks including: shuffle, execution model, and caching, by using a set of important analytic workloads. To conduct a detailed analysis, we developed two profiling tools: (1) We correlate the task execution plan with the resource utilization for both MapReduce and Spark, and visually present this correlation; (2) We provide a break-down of the task execution time for in-depth analysis. Through detailed experiments, we quantify the performance differences between MapReduce and Spark. Furthermore, we attribute these performance differences to different components which are architected differently in the two frameworks. We further expose the source of these performance differences by using a set of micro-benchmark experiments. Overall, our experiments show that Spark is about 2.5x, 5x, and 5x faster than MapReduce, for Word Count, k-means, and PageRank, respectively. The main causes of these speedups are the efficiency of the hash-based aggregation component for combine, as well as reduced CPU and disk overheads due to RDD caching in Spark. An exception to this is the Sort workload, for which MapReduce is 2x faster than Spark. We show that MapReduce's execution model is more efficient for shuffling data than Spark, thus making Sort run faster on MapReduce.

Document stores that provide the efficiency of a schema-less interface are widely used by developers in mobile and cloud applications. However, the simplicity developers achieved controversially leads to complexity for data management due to lack of a schema. In this paper, we present a schema management framework for document stores. This framework discovers and persists schemas of JSON records in a repository, and also supports queries and schema summarization. The major technical challenge comes from varied structures of records caused by the schema-less data model and schema evolution. In the discovery phase, we apply a canonical form based method and propose an algorithm based on equivalent sub-trees to group equivalent schemas efficiently. Together with the algorithm, we propose a new data structure, eSiBu-Tree, to store schemas and support queries. In order to present a single summarized representation for heterogenous schemas in records, we introduce the concept of "skeleton", and propose to use it as a relaxed form of the schema, which captures a small set of core attributes. Finally, extensive experiments based on real data sets demonstrate the efficiency of our proposed schema discovery algorithms, and practical use cases in real-world data exploration and integration scenarios are presented to illustrate the effectiveness of using skeletons in these applications.

Clipping Web pages, namely extracting the informative clips (areas) from Web pages, has many applications, such as Web printing and e-reading on small handheld devices. Although many existing methods attempt to address this task, most of them can either work only on certain types of Web pages (e.g., news- and blog-like web pages), or perform semi-automatically where extra user efforts are required in adjusting the outputs. The problem of clipping any types of Web pages accurately in a totally automatic way remains pretty much open. To this end in this study we harness the wisdom of the crowds to provide accurate recommendation of informative clips on any given Web pages. Specifically, we leverage the knowledge on how previous users clip similar Web pages, and this knowledge repository can be represented as a transaction database where each transaction contains the clips selected by a user on a certain Web page. Then, we formulate a new pattern mining problem, mining top-1 qualified pattern, on transaction database for this recommendation. Here, the recommendation considers not only the pattern support but also the pattern occupancy (proposed in this work). High support requires that patterns appear frequently in the database, while high occupancy requires that patterns occupy a large portion of the transactions they appear in. Thus, it leads to both precise and complete recommendation. Additionally, we explore the properties on occupancy to further prune the search space for high-efficient pattern mining. Finally, we show the effectiveness of the proposed algorithm on a human-labeled ground truth dataset consisting of 2000 web pages from 100 major Web sites, and demonstrate its efficiency on large synthetic datasets.

We describe a method to extract style and branding elements from multiple web pages in a given site for content repurposing. Style and branding elements convey the values of the site owners effectively and connect with the target prospects. They are manifested through logos, graphical elements, background color, font styles, font colors and other illustrations. Our method automatically extracts color and image elements appearing frequently and prominently on multiple pages throughout the site. We rely on a DOM tree matching method to obtain the frequency of re-occurring elements and use relative sizes and positions of elements to determine the type of elements. Note that approximate locations of these elements provide an added clue to the content repurposing engine as to where to place the elements in the repurposed document. The obtained results show that the proposed method can efficiently extract style and branding elements with high accuracy.