VerdictDB: Universalizing Approximate Query Processing

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171$\times$ speedup (18.45$\times$ on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.Comment: Extended technical report of the paper that appeared in Proceedings of the 2018 International Conference on Management of Data, pp. 1461-1476. ACM, 201

AQUAGP: Approximate QUery Answering Using Genetic Programming

Speed, cost, and accuracy are crucial performance parameters while evaluating the quality of a query using any Database Management System (DBMS). For some queries it may be possible to approximate the answer using an approximate query answering algorithm or tool. Also, for certain queries, it may not be critical to determine the perfect/exact results so long as the following conditions are true: (a) a high percentage of the relevant data is retrieved correctly, (b) irrelevant or extra data is minimized, and (c) an approximate answer (if available) results in a significant savings in terms of the overall query cost and retrieval time. In this paper we describe a novel approach for approximate query answering using the Genetic Programming (GP) paradigms. We develop an evolutionary computing based query space exploration framework. Given an input query and the database schema, our framework uses tree-based GP to automatically generate and evaluate approximate query candidates. We highlight and discuss different avenues we explored. We evaluate the success of our experiments based on the speed, the cost, and the accuracy of the results retrieved by the re-formulated (GP generated) queries and present the results on a variety of query types for TPC-benchmark and PKDD-benchmark datasets

Query Morphing: A Proximity-Based Approach for Data Exploration

We are living in age where large information in the form of structured and unstructured data is generated through social media, blogs, lab simulations, sensors etc. on daily basis. Due to this occurrences, acquisition of relevant information becomes a challenging task for humans. Fundamental understanding of complex schema and content is necessary for formulating data retrieval request. Therefore, instead of search, we need exploration in which a naïve user walks through the database and stops when satisfactory information is met. During this, a user iteratively transforms his search request in order to gain relevant information; morphing is an approach for generation of various transformation of input. We proposed ‘Query morphing’, an approach for query reformulation based on data exploration. Identified design concerns and implementation constraints are also discussed for the proposed approach

Ontology matching: state of the art and future challenges

shvaiko2013aInternational audienceAfter years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue some further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field