Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
132
result(s) for
"Querying (Computer science)"
Sort by:
Speech-to-SQL: toward speech-driven SQL query generation from natural language question
Speech-based inputs have been gaining significant momentum with the popularity of smartphones and tablets in our daily lives, since voice is the most popular and efficient way for human–computer interaction. This paper works toward designing more effective speech-based interfaces to query the structured data in relational databases. We first identify a new task named
Speech-to-SQL
, which aims to understand the information conveyed by human speech and directly translate it into structured query language (SQL) statements. A naive solution to this problem can work in a cascaded manner, that is, an automatic speech recognition component followed by a text-to-SQL component. However, it requires a high-quality ASR system and also suffers from the error compounding problem between the two components, resulting in limited performance. To handle these challenges, we propose a novel end-to-end neural architecture named
SpeechSQLNet
to directly translate human speech into SQL queries without an external ASR step. SpeechSQLNet has the advantage of making full use of the rich linguistic information presented in speech. To the best of our knowledge, this is the first attempt to directly synthesize SQL based on common natural language questions in spoken form, rather than a natural language-based version of SQL. To validate the effectiveness of the proposed problem and model, we further construct a dataset named
SpeechQL
, by piggybacking the widely used text-to-SQL datasets. Extensive experimental evaluations on this dataset show that SpeechSQLNet can directly synthesize high-quality SQL queries from human speech, outperforming various competitive counterparts as well as the cascaded methods in terms of exact match accuracies. We expect speech-to-SQL would inspire more research on more effective and efficient human–machine interfaces to lower the barrier of using relational databases.
Journal Article
A storage-efficient learned indexing for blockchain systems using a sliding window search enhanced online gradient descent
With its promise of transparency, security, and decentralization, blockchain technology faces significant challenges related to data storage and query efficiency. Current indexing methods, which often rely on structures like Merkle trees and Patricia tries, contribute to excessive storage overhead and slower query responses, particularly for full nodes that maintain a complete copy of the blockchain. To address this, we introduce a novel-learned indexing approach for blockchain that utilizes a layered structure with a sliding window search enhanced Online Gradient Descent (SWS-OGD) as the inter-block index. The method was implemented across five distinct blockchain environments—Bitcoin, Ethereum, Dogecoin, Litecoin, and IoTeX. Experimental results demonstrate that the proposed method reduces storage costs by up to 99% compared to state-of-the-art approaches, requiring as little as 0.9 KB for 20,000 blocks-a substantial improvement over existing methods. Despite the significant reduction in storage costs, the SWS-OGD method maintains comparable performance in other key metrics, such as query latency. These results ensure that blockchain systems can handle large-scale data queries efficiently, maintaining high performance even as the blockchain grows in size.
Journal Article
BestNeighbor: efficient evaluation of kNN queries on large time series databases
This paper presents parallel solutions (developed based on two state-of-the-art algorithms iSAX and sketch) for evaluating k nearest neighbor queries on large databases of time series, compares them based on various measures of quality and time performance, and offers a tool that uses the characteristics of application data to determine which algorithm to choose for that application and how to set the parameters for that algorithm. Specifically, our experiments show that: (i) iSAX and its derivatives perform best in both time and quality when the time series can be characterized by a few low-frequency Fourier Coefficients, a regime where the iSAX pruning approach works well. (ii) iSAX performs significantly less well when high-frequency Fourier Coefficients have much of the energy of the time series. (iii) A random projection approach based on sketches by contrast is more or less independent of the frequency power spectrum. The experiments show the close relationship between pruning ratio and time for exact iSAX as well as between pruning ratio and the quality of approximate iSAX. Our toolkit analyzes typical time series of an application (i) to determine optimal segment sizes for iSAX and (ii) when to use Parallel Sketches instead of iSAX. Our algorithms have been implemented using Spark, evaluated over a cluster of nodes, and have been applied to both real and synthetic data. The results apply to any databases of numerical sequences, whether or not they relate to time.
Journal Article
Beginning Entity Framework Core 2.0 : database access from .NET
\"Use the valuable Entity Framework Core 2.0 tool in ASP.NET and the .NET Framework to eliminate the tedium around accessing databases and the data they contain. Entity Framework Core 2.0 greatly simplifies access to relational databases such as SQL Server that are commonly deployed in corporate settings. By eliminating tedious data access code that developers are otherwise forced to use, Entity Framework Core 2.0 enables you to work directly with the data in a database through domain-specific objects and methods.\"-- Provided by publisher.
Book
Modeling and querying temporal RDF knowledge graphs with relational databases
RDF (Resource Description Framework), a standard resource description model, is popularized and applied in many application scenarios for its explicit representation of semantics. To represent and process time-aware semantics with RDF, the temporal RDF model is proposed and applied in temporal knowledge graphs. The requirement for efficiently handling diverse temporal RDF data has become increasingly important with the rapid development and popularity of RDF. In this paper, we propose a novel temporal RDF model and effectively tackle the management of temporal RDF data in relational databases. In particular, we propose a temporal RDF model called tRDF to represent both temporal entities and relationships and further propose a temporal query language for the tRDF model. To manage temporal RDF data in an effective manner, we propose to store temporal RDF data with relational databases that follow the SQL:2011 standard and support temporal data manipulation. To query the tRDF data stored in relational databases with the tRDF query language, we implement the transformation from this query language to SQL. The experimental results show the feasibility and effectiveness of the proposed tRDF model as well as its storage and query methods.
Journal Article
A natural language querying interface for process mining
In spite of recent advances in process mining, making this new technology accessible to non-technical users remains a challenge. Process maps and dashboards still seem to frighten many line of business professionals. In order to democratize this technology, we propose a natural language querying interface that allows non-technical users to retrieve relevant information and insights about their processes by simply asking questions in plain English. In this work we propose a reference architecture to support questions in natural language and provide the right answers by integrating to existing process mining tools. We combine classic natural language processing techniques (such as entity recognition and semantic parsing) with an abstract logical representation for process mining queries. We also provide a compilation of real natural language questions and an implementation of the architecture that interfaces to an existing commercial tool: Everflow. We also introduce a taxonomy for process mining related questions, and use that as a background grid to analyze the performance of this experiment. Finally, we point to potential future work opportunities in this field.
Journal Article