To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of graph databases have emerged, such as Neo4j and JanusGraph. They mainly target low-latency graph queries, such as finding the neighbors of a vertex with certain properties, and retrieving the shortest path between two vertices. Although many of the graph databases handle the graph-only queries very well, they fall short for real life applications involving graph analysis. This is because graph queries are not all that one does in an analytics workload of a real life application. They are often only a part of an integrated heterogeneous analytics pipeline, which may include SQL, machine learning, graph, and other analytics. This means graph queries need to be synergistic with other analytics. Unfortunately, most existing graph databases are standalone and cannot easily integrate with other analytics systems. In addition, many graph data (data about relationships between objects or people) are already prevalent in existing non-graph databases, although they are not explicitly stored as graphs. None of existing graph databases can retrofit graph queries onto these existing data without transferring or transforming data. In this paper, we propose an in-DBMS graph query approach, IBM Db2 Graph, to support synergistic and retrofittable graph queries inside the IBM Db2 relational database. It is implemented as a layer inside Db2, and thus can support integrated graph and SQL analytics efficiently. Db2 Graph employs a novel graph overlay approach to expose a graph view of the relational data. This approach flexibly retrofits graph queries to existing graph data stored in relational tables, without expensive data transfer or transformation. In addition, it enables efficient execution of graph queries with the help of Db2 relational engine, through sophisticated compile-time and runtime optimization strategies. Our experimental study, as well as our experience with real customers using Db2 Graph, showed that Db2 Graph can provide very competitive and sometimes even better performance on graph-only queries, compared to existing graph databases. Moreover, it optimizes the overall performance of complex analytics workloads.

To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of specialized graph databases have emerged, such as Neo4j, JanusGraph, and Sqlg. At the same time, RDBMSs and SQL continue to support mission-critical business analytics. However, real-life analytical applications seldom contain only one type of analytics. They are often made of heterogeneous workloads, including SQL, machine learning, graph, and other analytics. In particular, SQL and graph analytics are usually accompanied together in one analytical workload. This means that graph and SQL analytics need to be synergistic with each other. Unfortunately, most existing graph databases are standalone and cannot easily integrate with relational databases. In addition, as a matter of fact, many graph data (data about relationships between objects or people) are already prevalent in relational databases, although they are not explicitly stored as graphs. Performing graph analytics on these relational graph data today requires exporting large amount of data to the specialized graph databases. A natural question arises: can SQL and graph analytics be performed synergistically in a same system? In this demo, we present such a working system called IBM Db2 Graph. Db2 Graph is an in-DBMS graph query approach. It is implemented as a layer inside an experimental IBM Db2™, and thus can support synergistic graph and SQL analytics efficiently. Db2 Graph employs a graph overlay approach to expose a graph view of the relational data. This approach flexibly retrofits graph queries to existing graph data stored in relational tables. We use an example scenario on health insurance claim analysis to demonstrate how Db2 Graph is used to support synergistic graph and SQL analytics inside Db2.

As part of IBM's Smarter Planet initiative, we studied information-sharing practices in rural Northern China to understand if and how ICT can help rural residents improve their lives. Interviews and participant observation in two villages showed a profusion of ICT devices, as well as an abundance of face-to-face information exchanges, but a shortage of localized and easily accessible information, and a deep dependence of many rural residents on 'information brokers' such as agricultural extension workers and shop owners. Can ICT support existing practices of information sharing among rural residents, when these practices are largely based on face-to-face encounters and passive reception of information? We argue that in such an environment, ICT should build on existing social habits even if less than ideal, rather than trying to transform them, and we outline a possible way to do so.

Several test strategies have emerged to detect faults associated Boolean expressions. Current approaches lack a proper model to give an overall picture of the Boolean expressions and comprehensive exploration of test data space. This paper proposes a topological model (T-model) to systematically represent Boolean expressions and test data space, and theoretically analyzes the capability and limitations of existing test strategies. We explicitly identify the sufficient and necessary conditions to detect the faults of interest, introduce new Boolean expression related faults and reform the fault hierarchy, where a family of test strategies is defined to detect the corresponding faults.

While the traditional model driven development techniques are useful for building solutions in a reusable manner, they do not say much about how the existing assets in a client environment can be leveraged effectively and efficiently. In this work, we enhance model driven transformation techniques to generate implementation artifacts on a given platform from platform independent models while leveraging the existing assets in a client environment. We apply semantic Web service matching technology to achieve automatic binding of generated artifacts with available client assets. By generating implementation artifacts that are bound where appropriate with clientspsila existing functionality, our approach helps cut down the development time during project implementations and thereby resulting in reduced project durations and costs. We demonstrate the feasibility of the two platforms: IBM WebSphere and SAP NetWeaver. Lessons learned are presented.