Edison Lao Ting

Midas: integrating public financial data

The primary goal of the Midas project is to build a system that enables easy and scalable integration of unstructured and semi-structured information present across multiple data sources. As a first step in this direction, we have built a system that extracts and integrates information from regulatory filings submitted to the U.S. Securities and Exchange Commission (SEC) and the Federal Deposit Insurance Corporation (FDIC). Midas creates a repository of entities, events, and relationships by extracting, conceptualizing, integrating, and aggregating data from unstructured and semi-structured documents. This repository enables applications to use the extracted and integrated data in a variety of ways including mashups with other public data and complex risk analysis.

Extracting insights from social media with large-scale matrix approximations

Social media platforms such as blogs, Twitter® accounts, and online discussion sites are large-scale forums where every individual can potentially voice an influential public opinion. According to recent surveys, a massive number of Internet users are turning to such forums to collect recommendations and reviews for products and services, and to shape their individual choices and stances by the commentary of the online community as a whole. The unsupervised extraction of insight from unstructured user-generated web content requires new methodologies that are likely to be rooted in natural language processing and machine-learning techniques. Furthermore, the unprecedented scale of data begging to be analyzed necessitates the implementation of these methodologies on modern distributed computing platforms. In this paper, we describe a flexible new family of low-rank matrix approximation algorithms for modeling topics in a given corpus of documents (e.g., blog posts and tweets). We benchmark distributed optimization algorithms for running these models in a Hadoopi-enabled cluster environment. We describe online learning strategies for tracking the evolution of ongoing topics and rapidly detecting the emergence of new themes in a streaming setting.

Grouping and Optimization of XPath Expressions in System RX

Several XML DBMS support XQuery and/or SQL/XML languages, which are based on navigational primitives in the form of XPath expressions. Typically, these systems either model each XPath step as a separate query plan operator, or employ holistic approaches that can evaluate multiple steps of a single XPath expression. There have also been proposals to execute as many XPath expressions as possible within a single FLWOR block simultaneously in a data streaming context. We observe in our System-RX prototype that blindly combining all possible XPath expressions for concurrent execution can result in significant performance degradation. We identify two main problems. First, the simple strategy of grouping all XPath expressions on a single document does not always work if the query involves more than one data source or has nested query blocks. Second, merging XPath expressions may result in unnecessary execution of branches that can be filtered by predicates in other branches or elsewhere in the query. To rectify these problems, we develop a combination of heuristic- based rewrite transformations, to decide which XPath expressions should be grouped for concurrent evaluation, and cost-based optimization to globally order the groups within the query execution plan, and locally order the branches within individual groups. Experimental evaluation confirms that selectively grouping multiple XPath expressions allows for better query evaluation performance and reduces the query optimization complexity.