Atreyee Dey

Exploiting evidence from unstructured data to enhance master data management

Master data management (MDM) integrates data from multiple structured data sources and builds a consolidated 360-degree view of business entities such as customers and products. Todays MDM systems are not prepared to integrate information from unstructured data sources, such as news reports, emails, call-center transcripts, and chat logs. However, those unstructured data sources may contain valuable information about the same entities known to MDM from the structured data sources. Integrating information from unstructured data into MDM is challenging as textual references to existing MDM entities are often incomplete and imprecise and the additional entity information extracted from text should not impact the trustworthiness of MDM data. In this paper, we present an architecture for making MDM text-aware and showcase its implementation as IBM Info-Sphere MDM Extension for Unstructured Text Correlation, an add-on to IBM InfoSphere Master Data Management Standard Edition. We highlight how MDM benefits from additional evidence found in documents when doing entity resolution and relationship discovery. We experimentally demonstrate the feasibility of integrating information from unstructured data sources into MDM.

Fast Mining of Interesting Phrases from Subsets of Text Corpora

We address the problem of mining interesting phrases from subsets of a text corpus where the subset is specied using a set of features such as keywords that form a query. Previous algorithms for the problem have proposed solutions that involve sifting through a phrase dictionary based index or a document-based index where the solution is linear in either the phrase dictionary size or the size of the document subset. We propose the usage of an independence assumption between query keywords given the top correlated phrases, wherein the pre-processing could be reduced to discovering phrases from among the top phrases per each feature in the query. We then outline an indexing mechanism where per-keyword phrase lists are stored either in disk or memory, so that popular aggregation algorithms such as No Random Access and Sort-merge Join may be adapted to do the scoring at real-time to identify the top interesting phrases. Though such an approach is expected to be approximate, we empirically illustrate that very high accuracies (of over 90%) are achieved against the results of exact algorithms. Due to the simplied list-aggregation, we are also able to provide response times that are orders of magnitude better than state-of-the-art algorithms. Interestingly, our disk-based approach outperforms the in-memory baselines by up to hundred times and sometimes more, conrming the superiority of the proposed method.

Fission fragment mass distributions from Po210 and At213

Background: The influence of shell effect on the dynamics of the fusion fission process and its evolution with excitation energy in the pre-actinide Hg-Pb region in general is a matter of intense research in recent years. In particular, a strong ambiguity remains for the neutron shell closed

Deformation in28Si*produced via the16O+12C reaction

^{210}

Angular-momentum-gated light-particle evaporation spectra from97Tc*and62Zn*systems

Po nucleus regarding the role of shell effect in fission around

IDENTIFYING ENTITY MAPPINGS ACROSS DATA ASSETS

\approx

PRODUCT RECOMMENDATIONS OVER MULTIPLE STORES

30 - 40 MeV of excitation energy. Purpose: We have measured the fission fragment mass distribution of

Method for synthetic data generation for query workloads

^{210}

Fragment emission mechanism in the S32+C12 reaction

Po populated using fusion of

Contextualization of entity relationships

^{4}