Anno Langen

Google fusion tables: web-centered data management and collaboration

It has long been observed that database management systems focus on traditional business applications, and that few people use a database management system outside their workplace. Many have wondered what it will take to enable the use of data management technology by a broader class of users and for a much wider range of applications. Google Fusion Tables represents an initial answer to the question of how data management functionality that focused on enabling new users and applications would look in todays computing environment. This paper characterizes such users and applications and highlights the resulting principles, such as seamless Web integration, emphasis on ease of use, and incentives for data sharing, that underlie the design of Fusion Tables. We describe key novel features, such as the support for data acquisition, collaboration, visualization, and web-publishing.

Google fusion tables: data management, integration and collaboration in the cloud

Google Fusion Tables is a cloud-based service for data management and integration. Fusion Tables enables users to upload tabular data files (spreadsheets, CSV, KML), currently of up to 100MB. The system provides several ways of visualizing the data (e.g., charts, maps, and timelines) and the ability to filter and aggregate the data. It supports the integration of data from multiple sources by performing joins across tables that may belong to different users. Users can keep the data private, share it with a select set of collaborators, or make it public and thus crawlable by search engines. The discussion feature of Fusion Tables allows collaborators to conduct detailed discussions of the data at the level of tables and individual rows, columns, and cells. This paper describes the inner workings of Fusion Tables, including the storage of data in the system and the tight integration with the Google Maps infrastructure.

A high-performance, transactional filestore for application servers

There is a class of data, including messages and business workflow state, for which conventional monolithic databases are less than ideal. Performance and scalability of Application Server systems can be dramatically increased by distributing such data across transactional filestores, each of which is bound to a server instance in a cluster. This paper describes a high-performance, transactional filestore that has been developed for the BEA WebLogic Application ServerTM and benchmarks it against a database. The filestore uses a novel, platform-independent disk scheduling algorithm to minimize the latency of small, synchronous writes to disk.

Knowledge verification for long-tail verticals

Collecting structured knowledge for real-world entities has become a critical task for many applications. A big gap between the knowledge in existing knowledge repositories and the knowledge in the real world is the knowledge on tail verticals (i.e., less popular domains). Such knowledge, though not necessarily globally popular, can be personal hobbies to many people and thus collectively impactful. This paper studies the problem of knowledge verification for tail verticals; that is, deciding the correctness of a given triple. Through comprehensive experimental study we answer the following questions. 1) Can we find evidence for tail knowledge from an extensive set of sources, including knowledge bases, the web, and query logs? 2) Can we judge correctness of the triples based on the collected evidence? 3) How can we further improve knowledge verification on tail verticals? Our empirical study suggests a new knowledge-verification framework, which we call Facty, that applies various kinds of evidence collection techniques followed by knowledge fusion. Facty can verify 50% of the (correct) tail knowledge with a precision of 84%, and it significantly outperforms state-of-the-art methods. Detailed error analysis on the obtained results suggests future research directions.