Mobile applications, such as those on WebOS, increasingly depend on continuous analytics results of real-time events, for monitoring oil & gas production, watching traffic status and detecting accident, etc, which has given rise to the need of providing Continuous analytics as a Service (CaaaS). While representing a paradigm shift in cloud computing, CaaaS poses several challenges in scalability, latency, time-window semantics, transaction control and result-set staging. A data stream is infinite thus can only be analyzed in granules. We propose a continuous query model over both static relations and dynamic streaming data, which allows a long-standing SQL query instance to run cycle by cycle, each cycle for a chunk of data from the data stream, using a cut-and-rewind mechanism. We further support the cycle-based transaction model with cyclebased isolation and visibility, for delivering analytics results to the clients continuously while the query is running. To have the continuously generated analytics results staged efficiently, we developed the table-ring and label switching mechanism characterized by staging data through metadata manipulation without physical data moving and copying. To scale-out analytics computation, we support both parallel database based and network distributed Map-Reduce based infrastructure with multiple cooperating engines. We have built the proposed infrastructure by extending the PostgreSQL engine. We tested the throughput and latency of this service based on a well-known stream processing benchmark; the results show that the proposed approach is highly competitive. Our experiments indicate that the database technology can be extended and applied to real-time continuous analytics service provisioning.

QuickStart is a tool to automate the physical design of data warehouses for HP's Neoview system. It has been researched and prototyped at HP Labs with close interaction from Neoview design experts. It embodies heuristics and best practices of the experts to search for candidate physical features and uses cost calculations to recommend the features that result in good designs. It has some unique characteristics that differentiate it from other physical design advisors. In particular, it is the only advisor that is client-based, does not require a DBMS server installation and can work off a laptop by simply connecting to the customers' flat files. Another unique characteristic of QuickStart is that it provides the rationale for its recommendations.

While parallel DBMSs do support large scale parallel query processing on partitioned data, the reach of more general applications relies on User Defined Functions (UDFs). However, the existent UDF technology is insufficient both conceptually and practically. A UDF is not a relation-in, relation-out operator, which restricts its ability to model complex applications defined on a set of tuples rather than on a single one, and to be composed with other relational operators in a query. Further, to interact with the query execution efficiently, a UDF must be coded with complex interactions with DBMS internal data structures and system calls which is often beyond the expertise of an analytics application developer.

Relational queries on continuous streams of data are the subject of many recent database research projects. In 1998 a small group of people started a similar project with the goal to transform our product, NonStop SQL/MX, into an active RDBMS. This project tried to integrate functionality of transactional queuing systems with relational tables and with SQL, using simple extensions to the SQL syntax and guaranteeing clearly defined query and transactional semantics. The result is the first commercially available RDBMS that incorporates streams. All data flowing through the system is contained in relational tables and is protected by ACID transactions. Insert and update operations on any NonStop SQL table can be considered publishing of data and can therefore be transparent to the (legacy) applications performing them. Unlike triggers, the publish operation does not increase the path length of the application and it allows the subscriber to execute in a separate transaction. Subscribers, using an extended SQL syntax, see a continuous stream of data, consisting of all rows originally in the table plus all rows that are inserted or updated thereafter. The system scales by using partitioned tables and therefore partitioned streams.

Summary form only given, as follows. Research in the area of subquery unnesting algorithms has mostly focused on the problem of making queries more efficient at run-time by transforming subqueries into joins. Unnesting rules describe a transformation of a nested query tree or a nested SQL query into an equivalent tree or SQL query that is no longer nested. However, it is not possible to express all nested queries in a non-nested form, unless the used language (relational algebra or ISO/ANSI SQL) is extended. This means that a database system must continue to have the ability to process subqueries. When working on a new optimizer and executor design for NonStop SQL, our development team was faced with a slightly different problem: we wanted to eliminate the need for optimization and execution of nested queries altogether and were looking for a complete subquery unnesting process. Such a process would allow us to develop a query optimizer and executor that do not need to process subqueries. Our goal was to make use of the existing unnesting algorithms and to extend them in a way that does not necessarily improve or change the execution characteristics of nested queries, but that leads to complete unnesting of all forms of nested queries, as defined by the "full" level of the ISO/ANSI SQL92 standard. To indicate this different approach we call it "subquery elimination" rather than "subquery unnesting".