Related papers: Integration of Skyline Queries into Spark SQL
Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present new techniques…
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems.…
Snowflake revolutionized data analytics with an elastic architecture that decouples compute and storage, enabling scalable solutions supporting data architectures like data lake, data warehouse, data lakehouse, and data mesh. Building on…
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms. It allows deep learning…
Computer-based information technologies have been extensively used to help many organizations, private companies, and academic and education institutions manage their processes and information systems hereby become their nervous centre. The…
Data management applications are growing and require more attention, especially in the "big data" era. Thus, supporting such applications with novel and efficient algorithms that achieve higher performance is critical. Array database…
Skyline queries enable multi-criteria optimization by filtering objects that are worse in all the attributes of interest than another object. To handle the large answer set of skyline queries in high-dimensional datasets, the concept of…
Database queries traditionally operate under the closed-world assumption, providing no answers to questions that require information beyond the data stored in the database. Hybrid querying using SQL offers an alternative by integrating…
Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for computational performance. However, effectively performing…
Open and permissionless blockchains are distributed systems with thousands to tens of thousands of nodes, establishing novel platforms for decentralized applications. When realizing such an application, data might be stored and retrieved…
As the use of technology increases and data analysis becomes integral in many businesses, the ability to quickly access and interpret data has become more important than ever. Information retrieval technologies are being utilized by…
Although SPARQL has been the predominant query language over RDF graphs, some query intentions cannot be well captured by only using SPARQL syntax. On the other hand, the keyword search enjoys widespread usage because of its intuitive way…
While large-scale distributed data processing platforms have become an attractive target for query processing, these systems are problematic for applications that deal with nested collections. Programmers are forced either to perform…
Industrial AI systems are mostly end-to-end machine learning (ML) workflows. A typical recommendation or business intelligence system includes many online micro-services and offline jobs. We describe SQLFlow for developing such workflows…
Natural language is hypothetically the best user interface for many domains. However, general models that provide an interface between natural language and any other domain still do not exist. Providing natural language interface to…
Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations that are the basis for solving common machine learning…
Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from…
The rise of deep learning in natural language processing has fostered the creation of text to structured query language models composed of an encoder and a decoder. Researchers have experimented with various intermediate processing like…
BigBench is the new standard (TPCx-BB) for benchmarking and testing Big Data systems. The TPCx-BB specification describes several business use cases -- queries -- which require a broad combination of data extraction techniques including…
Deciding the equivalence of SQL queries is a fundamental problem in data management. As prior work has mainly focused on studying the theoretical limitations of the problem, very few implementations for checking such equivalences exist. In…