Data Mining the SDSS SkyServer Database

An earlier paper (Szalay et. al. "Designing and Mining MultiTerabyte Astronomy Archives: The Sloan Digital Sky Survey," ACM SIGMOD 2000) described the Sloan Digital Sky Survey's (SDSS) data management needs by defining twenty database queries and twelve data visualization tasks that a good data management system should support. We built a database and interfaces to support both the query load and also a website for ad-hoc access. This paper reports on the database design, describes the data loading pipeline, and reports on the query implementation and performance. The queries typically translated to a single SQL statement. Most queries run in less than 20 seconds, allowing scientists to interactively explore the database. This paper is an in-depth tour of those queries. Readers should first have studied the companion overview paper Szalay et. al. "The SDSS SkyServer, Public Access to the Sloan Digital Sky Server Data" ACM SIGMOND 2002.Comment: 40 pages, Original source is at http://research.microsoft.com/~gray/Papers/MSR_TR_O2_01_20_queries.do

Design and Implementation of an Enterprise Data Warehouse

The reporting and sharing of information has been synonymous with databases as long as there have been systems to host them. Now more than ever, users expect the sharing of information in an immediate, efficient, and secure manner. However, due to the sheer number of databases within the enterprise, getting the data in an effective fashion requires a coordinated effort between the existing systems. There is a very real need today to have a single location for the storage and sharing of data that users can easily utilize to make improved business decisions, rather than trying to traverse the multiple databases that exist today and can do so by using an enterprise data warehouse. The Thesis involves a description of data warehousing techniques, design, expectations, and challenges regarding data cleansing and transforming existing data, as well as other challenges associated with extracting from transactional databases. The Thesis also includes a technical piece discussing database requirements and technologies used to create and refresh the data warehouse. The Thesis discusses how data from databases and other data warehouses could integrate. In addition, there is discussion of specific data marts within the warehouse to satisfy a specific need. Finally, there are explanations for how users will consume the data in the enterprise data warehouse, such as through reporting and other business intelligence. This discussion also includes the topics of system architecture of how data from databases and other data warehouses from different departments could integrate. An Enterprise Data Warehouse prototype developed will show how a pair of different databases undergoes the Extract, Transform and Load (ETL) process and loaded into an actual set of star schemas then makes the reporting easier. Separately, an important piece of this thesis takes an actual example of data and compares the performance between them by running the same queries against separate databases, one transactional and one data warehouse. As the queries expand in difficulty, larger grows the gap between the actual recorded times of running that same query in the different environments

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Advances in Large-Scale RDF Data Management

One of the prime goals of the LOD2 project is improving the performance and scalability of RDF storage solutions so that the increasing amount of Linked Open Data (LOD) can be efficiently managed. Virtuoso has been chosen as the basic RDF store for the LOD2 project, and during the project it has been significantly improved by incorporating advanced relational database techniques from MonetDB and Vectorwise, turning it into a compressed column store with vectored execution. This has reduced the performance gap (“RDF tax”) between Virtuoso’s SQL and SPARQL query performance in a way that still respects the “schema-last” nature of RDF. However, by lacking schema information, RDF database systems such as Virtuoso still cannot use advanced relational storage optimizations such as table partitioning or clustered indexes and have to execute SPARQL queries with many self-joins to a triple table, which leads to more join effort than needed in SQL systems. In this chapter, we first discuss the new column store techniques applied to Virtuoso, the enhancements in its cluster parallel version, and show its performance using the popular BSBM benchmark at the unsurpassed scale of 150 billion triples. We finally describe ongoing work in deriving an “emergent” relational schema from RDF data, which can help to close the performance gap between relational-based and RDF-based storage solutions

How the High Performance Analytics Work with SAP HANA

Informed decision-making, better communication and faster response to business situation are the key differences between leaders and followers in this competitive global marketplace. A data-driven organization can analyze patterns & anomalies to make sense of the current situation and be ready for future opportunities. Organizations no longer have the problem of “lack of data”, but the problem of “actionable data” at the right time to act, direct and influence their business decisions. The data exists in different transactional systems and/or data warehouse systems, which takes significant time to retrieve/ process relevant information and negatively impacts the time window to out-maneuver the competition. To solve the problem of “actionable data”, enterprises can take advantage of the SAP HANA [1] in-memory platform that enables rapid processing and analysis of huge volumes of data in real-time. This paper discusses how SAP HANA virtual data models can be used for on-the-fly analysis of live transactional data to derive insight, perform what-if analysis and execute business transactions in real-time without using persisted aggregates

Attribute oriented induction with star schema

This paper will propose a novel star schema attribute induction as a new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using non rule based concept hierarchy by implementing both of approaches. In novel star schema attribute induction some improvements have been implemented like elimination threshold number as maximum tuples control for generalization result, there is no ANY as the most general concept, replacement the role concept hierarchy with concept tree, simplification for the generalization strategy steps and elimination attribute oriented induction algorithm. Novel star schema attribute induction is more powerful than the current attribute oriented induction since can produce small number final generalization tuples and there is no ANY in the results.Comment: 23 Pages, IJDM

SODA: Generating SQL for Business Users

The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need answers to ad-hoc queries that were not anticipated. This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.Comment: VLDB201