Declarative Ajax Web Applications through SQL++ on a Unified Application State

Implementing even a conceptually simple web application requires an inordinate amount of time. FORWARD addresses three problems that reduce developer productivity: (a) Impedance mismatch across the multiple languages used at different tiers of the application architecture. (b) Distributed data access across the multiple data sources of the application (SQL database, user input of the browser page, session data in the application server, etc). (c) Asynchronous, incremental modification of the pages, as performed by Ajax actions. FORWARD belongs to a novel family of web application frameworks that attack impedance mismatch by offering a single unifying language. FORWARD's language is SQL++, a minimally extended SQL. FORWARD's architecture is based on two novel cornerstones: (a) A Unified Application State (UAS), which is a virtual database over the multiple data sources. The UAS is accessed via distributed SQL++ queries, therefore resolving the distributed data access problem. (b) Declarative page specifications, which treat the data displayed by pages as rendered SQL++ page queries. The resulting pages are automatically incrementally modified by FORWARD. User input on the page becomes part of the UAS. We show that SQL++ captures the semi-structured nature of web pages and subsumes the data models of two important data sources of the UAS: SQL databases and JavaScript components. We show that simple markup is sufficient for creating Ajax displays and for modeling user input on the page as UAS data sources. Finally, we discuss the page specification syntax and semantics that are needed in order to avoid race conditions and conflicts between the user input and the automated Ajax page modifications. FORWARD has been used in the development of eight commercial and academic applications. An alpha-release web-based IDE (itself built in FORWARD) enables development in the cloud.Comment: Proceedings of the 14th International Symposium on Database Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento, Ital

Anytime Hierarchical Clustering

We propose a new anytime hierarchical clustering method that iteratively transforms an arbitrary initial hierarchy on the configuration of measurements along a sequence of trees we prove for a fixed data set must terminate in a chain of nested partitions that satisfies a natural homogeneity requirement. Each recursive step re-edits the tree so as to improve a local measure of cluster homogeneity that is compatible with a number of commonly used (e.g., single, average, complete) linkage functions. As an alternative to the standard batch algorithms, we present numerical evidence to suggest that appropriate adaptations of this method can yield decentralized, scalable algorithms suitable for distributed/parallel computation of clustering hierarchies and online tracking of clustering trees applicable to large, dynamically changing databases and anomaly detection.Comment: 13 pages, 6 figures, 5 tables, in preparation for submission to a conferenc

QUASII: QUery-Aware Spatial Incremental Index.

With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can be executed efficiently. Such an indexing step requires substantial computing resources and introduces a considerable and growing data-to-insight gap where scientists need to wait before they can perform any analysis. Moreover, scientists often only use a small fraction of the data - the parts containing interesting phenomena - and indexing it fully does not always pay off. In this paper we develop a novel incremental index for the exploration of spatial data. Our approach, QUASII, builds a data-oriented index as a side-effect of query execution. QUASII distributes the cost of indexing across all queries, while building the index structure only for the subset of data queried. It reduces data-to-insight time and curbs the cost of incremental indexing by gradually and partially sorting the data, while producing a data-oriented hierarchical structure at the same time. As our experiments show, QUASII reduces the data-to-insight time by up to a factor of 11.4x, while its performance converges to that of the state-of-the-art static indexes