Vishal S. Batra

Information extraction from biomedical literature: methodology, evaluation and an application

Journals and conference proceedings represent the dominant mechanisms of reporting new biomedical results. The unstructured nature of such publications makes it difficult to utilize data mining or automated knowledge discovery techniques. Annotation (or markup) of these unstructured documents represents the first step in making these documents machine analyzable. In this paper we first present a system called BioAnnotator for identifying and annotating biological terms in documents. BioAnnotator uses domain based dictionary look-up for recognizing known terms and a rule engine for discovering new terms. The combination and dictionary look-up and rules result in good performance (87% precision and 94% recall on the GENIA 1.1 corpus for extracting general biological terms based on an approximate matching criterion). To demonstrate the subsequent mining and knowledge discovery activities that are made feasible by BioAnnotator, we also present a system called MedSummarizer that uses the extracted terms to identify the common concepts in a given group of genes.

Load Balancing for Multi-tiered Database Systems through Autonomic Placement of Materialized Views

A materialized view or Materialized Query Table (MQT) is an auxiliary table with precomputed data that can be used to significantly improve the performance of a database query. AMaterialized Query Table Advisor (MQTA) is often used to recommend and create MQTs. The state-of-the-art MQTA works in a standalone database server where MQTs are placed on the same server as that in which the base tables are located. The MQTA does not apply to a federated or scaleout scenario in which MQTs need to be placed on other servers close to applications (i.e. a frontend database server) for offloading the workload on the backend database server. In this paper, we propose a Data Placement Advisor (DPA) and load balancing strategies for multi-tiered database systems. Built on top of the MQTA, DPA recommends MQTs and advises placement strategies for minimizing the response time for a query workload. To demonstrate the benefit of the data placement advising, we implemented a prototype of DPA that works with theMQTA in the IBM® DB2® Universal Database^TM (DB2 UDB) and the IBM WebSphere® Information Integrator (WebSphere II). The evaluation results showed substantial improvements of workload response times when MQTs are intelligently recommended and placed at a frontend database server subject to space and load characteristics for TPC-H and OLAP type workloads.

Towards a Shared Ledger Business Collaboration Language Based on Data-Aware Processes

Shared ledger technologies, as exemplified by Blockchain, provide a new framework for supporting business collaborations that is based on having a high-reliability, shared, trusted, privacy-preserving, nonrepudiable data repository that includes programmable logic in the form of “smart contracts”. The framework has the potential to dramatically transform business collaboration across numerous industry sectors, including finance, supply chain, food production, pharmaceuticals, and healthcare. Widespread adoption of this technology will be accelerated by the development of business-level languages for specifying smart contracts. This paper proposes that data-aware business processes, and in particular the Business Artifact paradigm, can provide a robust basis for a shared ledger Business Collaboration Language (BCL). The fundamental rationale for adopting data-aware processes is that shared ledgers focus on both data and process in equal measure. The paper examines potential advantages of the artifact-based approach from two perspectives: conceptual modeling, and opportunities for formal reasoning (verification). Broad research challenges for the development, understanding, and usage of a shared ledger BCL are highlighted.

Load balancing and data placement for multi-tiered database systems

A materialized view or Materialized Query Table (MQT) is an auxiliary table with precomputed data that can be used to significantly improve the performance of a database query. A Materialized Query Table Advisor (MQTA) is often used to recommend and create MQTs. The state-of-the-art MQTA works in a standalone database server where MQTs are placed on the same server as that in which the base tables are located. The MQTA does not apply to a federated or scaleout scenario in which MQTs need to be placed on other servers close to applications (i.e. a frontend database server) for offloading the workload on the backend database server. In this paper, we propose a Data Placement Advisor (DPA) and load balancing strategies for multi-tiered database systems. Built on top of the MQTA, DPA recommends MQTs and advises placement strategies for minimizing the response time for a query workload. To demonstrate the benefit of the data placement advising, we implemented a prototype of DPA that works with the MQTA in the IBM^(R) DB2^(R) Universal Database(TM) (DB2 UDB) and the IBM WebSphere^(R) Information Integrator (WebSphere II). The evaluation results showed substantial improvements of workload response times when MQTs are intelligently recommended and placed on a frontend database server subject to space and load characteristics for TPC-H and OLAP type workloads.

A system for knowledge management in bioinformatics

The emerging biochip technology has made it possible to simultaneously study expression (activity level) of thousands of genes or proteins in a single experiment in the laboratory. However, in order to extract relevant biological knowledge from the biochip experimental data, it is critical not only to analyze the experimental data, but also to cross-reference and correlate these large volumes of data with information available in external biological databases accessible online. We address this problem in a comprehensive system for knowledge management in bioinformatics called e2e. To the biologist or biological applications, e2e exposes a common semantic view of inter-relationship among biological concepts in the form of an XML representation called eXpressML, while internally, it can use any data integration solution to retrieve data and return results corresponding to the semantic view. We have implemented an e2e prototype that enables a biologist to analyze her gene expression data in GEML or from a public site like Stanford, and discover knowledge through operations like querying on relevant annotated data represented in eXpressML using pathways data from KEGG, publication data from Medline and protein data from SWISS-PROT.

Edge analytics as service — A service oriented framework for real time and personalised recommendation analytics

Due to the advent of technology and internet over the past few years, significant number of customers have started shopping online and accessing their bank account through various channels like Netbanking, Mobile banking etc. In this paper, we describe Edge Analytics framework which deliver analytics as a service that can be hosted by a financial institute like Bank for delivering personalized offer in real time on thier netbanking portals or on other channels. This edge analytics service can be accessed through edge APIs plugged into netbanking portals. It access the recent transactions in the log to determine offers for the customer and therefore, saving a lot of resources and effort by fetching the data from the main warehouse. Edge analytics server capability has been enhanced by incorprating knowledge such as users intent and interest from their social media profile by identifying their identity on the Online social network. This information is then feed into rule engine to generate customised offers for each user using both enterprise and social information. Edge Analytics service has been hosted on cloud which renders it scalability factor and allows different third party provders to make use of the service easily..

D’MART: A Tool for Building and Populating Data Warehouse Model from Existing Reports and Tables

As companies grow (organically or inorganically), Data Administration (i.e. Stage 5 of Nolan’s IT growth model) becomes the next logical step in their IT evolution. Designing a Data Warehouse model, especially in the presence of legacy systems, is a challenging task. A lot of time and effort is consumed in understanding the existing data requirements, performing Dimensional and Fact modeling etc. This problem is further exacerbated if enterprise outsource their IT needs to external vendors. In such a situation no individual has a complete and in-depth view of the existing data setup. For such settings, a tool that can assist in building a data warehouse model from existing data models such that there is minimal impact to the business can be of immense value. In this paper we present the D’MART tool which addresses this problem. D’MART analyzes the existing data model of the enterprise and proposes alternatives for building the new data warehouse model. D’MART models the problem of identifying Fact/Dimension attributes of a warehouse model as a graph cut on a Dependency Analysis Graph (DAG). The DAG is built using the existing data models and the BI Report generation (SQL) scripts. The D’MART tool also uses the DAG for generation of ETL scripts that can be used to populate the newly proposed data warehouse from data present in the existing schemas. D’MART was developed and validated as part of an engagement with Indian Railways which operates one of the largest rail networks in the world.

Simultaneously improving CSAT and profit in a retail banking organization

Customer satisfaction (CSAT) is the key driver for retention and growth in retail banking and several techniques have been applied by banks to achieve this. For instance, banks in emerging markets with high footfall in branches have gone beyond the traditional approach of segmenting customers and services to optimizing the wait time for customers visiting the banks branch. While this approach has significantly improved service quality, it has also added a new dimension in the service quality metric : pro-actively identify and address customer needs for (i) efficient banking experience and (ii) enhancing profit by selling additional services to existing customer. In this paper we present a system that addresses the challenge involved in providing better service to retail banking customer while ensuring that a larger share of customers wallet comes to the branch. We do this by combining predictive analytics, scheduling and process optimization techniques.

J2EE Architecture for Database Cluster-Based High Volume E-Commerce Web Applications

High volume database-driven e-commerce applications demand a cluster-based infrastructure to offers high availability, scalability and fault tolerance. The current J2EE architecture and containers restrict the transparent deployment of applications over database clusters without engineering data access logic into the applications. Our work extends the J2EE architecture to allow transparent deployment of J2EE applications on a database cluster. The key challenge is to load balance read and write queries between the master and replica database instance and yet provide the application with the most recent data in the cluster while enabling service class based query routing. We validate the applicability and effectiveness of the proposed architecture using IBM WebSphere Trade3 stock trading application.