Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCortex: Harnessing Correlations to Boost Query Performance
170
0
0.0
(
0
)
Added by
Vikram Nathan
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Databases employ indexes to filter out irrelevant records, which reduces scan overhead and speeds up query execution. However, this optimization is only available to queries that filter on the indexed attribute. To extend these speedups to queries on other attributes, database systems have turned to secondary and multi-dimensional indexes. Unfortunately, these approaches are restrictive: secondary indexes have a large memory footprint and can only speed up queries that access a small number of records, and multi-dimensional indexes cannot scale to more than a handful of columns. We present Cortex, an approach that takes advantage of correlations to extend the reach of primary indexes to more attributes. Unlike prior work, Cortex can adapt itself to any existing primary index, whether single or multi-dimensional, to harness a broad variety of correlations, such as those that exist between more than two attributes or have a large number of outliers. We demonstrate that on real datasets exhibiting these diverse types of correlations, Cortex matches or outperforms traditional secondary indexes with $5times$ less space, and it is $2-8times$ faster than existing approaches to indexing correlations.
rate research
Read More
Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing - Query Planning - is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including DBpedia, YAGO, and Freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.
XML data warehouses form an interesting basis for decision-support applications that exploit heterogeneous data from multiple sources. However, XML-native database systems currently suffer from limited performances in terms of manageable data volume and response time for complex analytical queries. Fragmenting and distributing XML data warehouses (e.g., on data grids) allow to address both these issues. In this paper, we work on XML warehouse fragmentation. In relational data warehouses, several studies recommend the use of derived horizontal fragmentation. Hence, we propose to adapt it to the XML context. We particularly focus on the initial horizontal fragmentation of dimensions XML documents and exploit two alternative algorithms. We experimentally validate our proposal and compare these alternatives with respect to a unified XML warehouse model we advocate for.
As Knowledge Graphs (KGs) continue to gain widespread momentum for use in different domains, storing the relevant KG content and efficiently executing queries over them are becoming increasingly important. A range of Data Management Systems (DMSs) have been employed to process KGs. This paper aims to provide an in-depth analysis of query performance across diverse DMSs and KG query types. Our aim is to provide a fine-grained, comparative analysis of four major DMS types, namely, row-, column-, graph-, and document-stores, against major query types, namely, subject-subject, subject-object, tree-like, and optional joins. In particular, we analyzed the performance of row-store Virtuoso, column-store Virtuoso, Blazegraph (i.e., graph-store), and MongoDB (i.e., document-store) using five well-known benchmarks, namely, BSBM, WatDiv, FishMark, BowlognaBench, and BioBench-Allie. Our results show that no single DMS displays superior query performance across the four query types. In particular, row- and column-store Virtuoso are a factor of 3-8 faster for tree-like joins, Blazegraph performs around one order of magnitude faster for subject-object joins, and MongoDB performs over one order of magnitude faster for high-selective queries.
The query log of a DBMS is a powerful resource. It enables many practical applications, including query optimization and user experience enhancement. And yet, mining SQL queries is a difficult task. The fundamental problem is that queries are symbolic objects, not vectors of numbers. Therefore, many popular statistical concepts, such as means, regression, or decision trees do not apply. Most authors limit themselves to ad hoc algorithms or approaches based on neighborhoods, such as k Nearest Neighbors. Our project is to challenge this limitation. We introduce methods to manipulate SQL queries as if they were vectors, thereby unlocking the whole statistical toolbox. We present three families of methods: feature maps, kernel methods, and Bayesian models. The first technique directly encodes queries into vectors. The second one transforms the queries implicitly. The last one exploits probabilistic graphical models as an alternative to vector spaces. We present the benefits and drawbacks of each solution, highlight how they relate to each other, and make the case for future investigation.
The amount of textual data has reached a new scale and continues to grow at an unprecedented rate. IBMs SystemT software is a powerful text analytics system, which offers a query-based interface to reveal the valuable information that lies within these mounds of data. However, traditional server architectures are not capable of analyzing the so-called Big Data in an efficient way, despite the high memory bandwidth that is available. We show that by using a streaming hardware accelerator implemented in reconfigurable logic, the throughput rates of the SystemTs information extraction queries can be improved by an order of magnitude. We present how such a system can be deployed by extending SystemTs existing compilation flow and by using a multi-threaded communication interface that can efficiently use the bandwidth of the accelerator.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
