Deep Reinforcement Learning for Join Order Enumeration

Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they do not have a mechanism for "learning from their mistakes". In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve decision making by incorporating feedback from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, and present preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency

Detecting behavioral conflicts among crosscutting concerns

Aspects have been successfully promoted as a means to improve the modularization of software in the presence of crosscutting concerns. Within the Ideals project, aspects have been shown to be valuable for improving the modularization of idioms (see also Chapter 1). The so-called aspect interference problem is considered to be one of the remaining challenges of aspect-oriented software development: aspects may interfere with the behavior of the base code or other aspects. Especially interference among aspects is difficult to prevent, as this may be caused solely by the composition of aspects that behave correctly in isolation. A typical situation where this may occur is when multiple advices are applied at the same, or shared, join point. In this chapter we explain the problem of behavioral conflicts among aspects at shared join points, illustrated by aspects that represent idioms: Parameter checking and Error propagation. We present an approach for the detection of behavioral conflicts that is based on a novel abstraction model for representing the behavior of advice. The approach employs a set of conflict detection rules which can be used to detect both generic conflicts as well as domain or application specific conflicts. One of the benefits of the approach is that it neither requires the application programmers to deal with the conflict models, nor does it require a background in formal methods for the aspect programmers

TOPYDE: A Tool for Physical Database Design

We describe a tool for physical database design based on a combination of theoretical and pragmatic approaches. The tool takes as input a relational schema, the workload defined on the schema, and some additional database characteristics and produces as output a physical schema. For the time being, the tool is tuned towards Ingres

Static and Dynamic Detection of Behavioral Conflicts Between Aspects

Aspects have been successfully promoted as a means to improve the modularization of software in the presence of crosscutting concerns. The so-called aspect interference problem is considered to be one of the remaining challenges of aspect-oriented software development: aspects may interfere with the behavior of the base code or other aspects. Especially interference between aspects is difficult to prevent, as this may be caused solely by the composition of aspects that behave correctly in isolation. A typical situation where this may occur is when multiple advices are applied at a shared, join point.\ud In [1] we explained the problem of behavioral conflicts between aspects at shared join points. We presented an approach for the detection of behavioral conflicts. This approach is based on a novel abstraction model for representing the behavior of advice. This model allows the expression of both primitive and complex behavior in a simple manner. This supports automatic conflict detection. The presented approach employs a set of conflict detection rules, which can be used to detect generic, domain specific and application specific conflicts. The approach is implemented in Compose*, which is an implementation of Composition Filters. This application shows that a declarative advice language can be exploited for aiding automated conflict detection.\ud This paper discusses the need for a runtime extension to the described static approach. It also presents a possible implementation approach of such an extension in Compose*. This allows us to reason efficiently about the behavior of aspects. It also enables us to detect these conflicts with minimal overhead at runtime

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201