Huawei's cloud-native database system GaussDB for MySQL (also known as Taurus) stores data in a separate storage layer consisting of a pool of storage servers. Each server has considerable compute power making it possible to push data reduction operations (selection, projection, and aggregation) close to storage. This paper describes the design and implementation of near data processing (NDP) in Taurus. NDP has several benefits: it reduces the amount of data shipped over the network; frees up CPU capacity in the compute layer; and reduces query run time, thereby enabling higher system throughput. Experiments with the TPC-H benchmark (100 GB) showed that 18 out of 22 queries benefited from NDP; data shipped was reduced by 63%; and CPU time by 50%. On Q15 the impact was even higher: data shipped was reduced by 98%; CPU time by 91%; and run time by 80%.

Automatic anomaly detection is a hard but practically useful problem. With telemetry data sizes growing constantly, experts will rely increasingly on automation to bring anomalies to their attention. In this paper, anomaly transition points (called change points elsewhere), are determined using a novel application of a somewhat obscure statistical score called “z-score of mean difference”. Use of this score yields a practical linear-time algorithm called LRZ Convolution with sound statistical underpinnings, and which does not require data normality. Each anomaly transition point is accompanied by a set of explanatory predicates that can form a good starting point for determining an anomaly’s root causes. Careful experimental evaluation and performance in two independent domains show promising results. A preliminary comparison with a well-known machine learning algorithm called Support Vector Machines (SVM) yields a highly favorable outcome.

Database Tuning Advisor (DTA) is a physical database design tool that is part of Microsoft's SQL Server 2005 relational database management system. Previously known as "Index Tuning Wizard" in SQL Server 7.0 and SQL Server 2000, DTA adds new functionality that is not available in other contemporary physical design tuning tools. Novel aspects of DTA that will be demonstrated include: (a) Ability to take into account both performance and manageability requirements of DBAs (b) Fully integrated recommendations for indexes, materialized views and horizontal partitioning (c) Transparently leverage a test server to offload tuning load from production server and (d) Easy programmability and scriptability.

Arrays are a common and important class of data. They can model digital images, digital video, scientific and experimentation data, matrices, finite element grids, and many other types of data. Although array manipulations are diverse and domain-specific, they often exhibit structural regularities. The paper presents an algorithm called SUN-pushdown to compute data lineage in such array computations. The array manipulations are expressed in the Array Manipulation Language (AML) that was introduced previously (A.P. Marathe and K. Salem, 1997). SUB-pushdown has several useful features. First, the lineage computation is expressed as an AML query. Second, it is not necessary to evaluate the AML lineage query to compute the array data lineage. Third, SUB-pushdown never gives false-negative answers. SUB-pushdown has been implemented as part of the ArrayDB prototype array database system that we built (A.P. Marathe, 2001).

Arrays are an appropriate data model for images, gridded output from computational models, and other types of data. This paper describes an approach to array query processing. Queries are expressed in AML, a logical algebra that is easily extended with user-defined functions to support a wide variety of array operations. For example, compression, filtering, and algebraic operations on images can be described. We show how AML expressions involving such operations can be treated declaratively and subjected to useful rewrite optimizations. We also describe a plan generator that produces efficient iterator-based plans from rewritten AML expressions.