The IBM Db2 Analytics Accelerator (IDAA) is a state-of-the art hybrid database system that seamlessly extends the strong transactional capabilities of Db2 for z/OS with the very fast column-store processing in Db2 Warehouse. The Accelerator maintains a copy of the data from Db2 for z/OS in its backend database. Data can be synchronized at a single point in time with a granularity of a table, one or more of its partitions, or incrementally as rows changed using replication technology. IBM Change Data Capture (CDC) has been employed as replication technology since IDAA version 3. Version 7.5.0 introduces a superior replication technology as a replacement for IDAA's use of CDC - Integrated Synchronization. In this paper, we present how Integrated Synchronization improves the performance by orders of magnitudes, paving the way for near real-time Hybrid Transactional and Analytical (HTAP) processing.

The IBM DB2 Analytics Accelerator (IDAA) integrates the strong OLTP capabilities of DB2 for z/OS with very fast processing of OLAP workloads using Netezza technology. The accelerator is attached to DB2 as analytical process- ing resource { completely transparent for user applications. But all data modi_cations must be carried out by DB2 and are replicated to the accelerator internally. However, this behavior is not optimized for ELT processing and predic- tive analytics or data mining workloads where multi-staged data transformations are involved. We present our work for extending IDAA with accelerator-only tables, which enable direct data transformations without any necessary interven- tions by DB2. Further, we present a framework for executing arbitrary in-database analytics operations on the accelerator while ensuring data governance aspects like privilege man- agement on DB2 and allowing to ingest data from any other source directly to the accelerator to enrich analytics e. g., with social media data. The evolutionary framework design maintains compatibility with existing infrastructure and ap- plications, a must-have for the majority of customers, while allowing complex analytics beyond read-only reporting.

Usability often plays a key role when software is brought to market, including clearly structured workflows, the way of presenting information to the user, and, last but not least, how he interacts with the application. In this context, input devices as 3D cameras or (multi-)touch displays became om-nipresent in order to define new intuitive ways of user inter-action. State-of-the-art systems tightly couple application logic with separate gesture detection components for sup-ported devices. Hard-coded rules or static models obtained by applying machine learning algorithms on many training samples are used in order to robustly detect a pre-defined set of gesture patterns. If possible at all, it becomes difficult to extend these sets with new patterns or to modify exist-ing ones – difficult for both, application developers and end users. Further, adding gesture support for legacy software or for additional devices becomes difficult with this hard-wired approach. In previous research we demonstrated how the database community can contribute to this challenge by leveraging complex event processing on data streams to express gesture patterns. While this declarative approach decouples application logic from gesture detection compo-nents, its major drawback was the non-intuitive definition of gesture queries. In this paper, we present an approach that is related to density-based clustering in order to find declarative gesture descriptions using only a few samples. We demonstrate the algorithms on mining definitions for multi-dimensional gestures from the sensor data stream that is delivered by a Microsoft Kinect 3D camera, and provide a way for non-expert users to intuitively customize gesture-controlled user interfaces – even during runtime. 1.

Efficient lookups in huge, possibly multi-dimensional datasets are crucial for the performance of numerous use cases that generate multiple search operations at the same time, like point queries in ray tracing or spatial joins in collision detection of interactive 3D applications. These applications greatly benefit from index structures that quickly filter relevant candidates for further processing. Since different lookup operations are independent from each other, they might be processed in parallel on modern hardware like multi-core CPUs or GPUs. But implementing efficient algorithms for all kinds of indexes on various hardware platforms is a challenging task. In this paper, we present a new approach that extends the existing GiST index framework with an abstraction layer for the hardware where index operations are executed. Furthermore, we provide first performance evaluations for the scan execution on CPUs and an Nvidia Tesla GPU.

Query performance is a critical factor in modern business intelligence and data warehouse systems. An increasing number of companies uses detailed analyses for conducting daily business and supporting management decisions. Thus, several techniques have been developed for achieving near realtime response times - techniques which try to alleviate I/O bottlenecks while increasing the throughputs of available processing units, i.e. by keeping relevant data in compressed main-memory data structures and exploiting the read-only characteristics of analytical workloads.