Related papers: bdbms -- A Database Management System for Biologic…
With the rapid increasing of data scale, in-database analytics and learning has become one of the most studied topics in data science community, because of its significance on reducing the gap between the management and the analytics of…
The merger of computer science, mathematics, and life sciences has brought about the discipline known as bioinformatics. However, the transmission (e.g. training, learning, and teaching) of this knowledge becomes an important issue. Many…
Spurred by a number of recent trends, we make the case that the relational database systems should urgently move beyond supporting the basic object-relational model and instead embrace a more abstract data model, specifically, the…
Modern tools for biological research, especially microscopy, have rapidly advanced in recent years, which has led to the generation of increasingly large amounts of data on a regular basis. The result is that scientists desperately need…
Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality? This paper describes BayesDB, a probabilistic programming platform that aims to enable users…
Product Data Management (PDM) claims of producing desktop and web based systems to maintain the organizational data to increase the quality of products by improving the process of development, business process flows, change management,…
Database Management Systems (DBMSs) are widely used to store, retrieve, and manage the data handled by modern applications. Although prior work has studied the co-evolution of DBMSs and application source code, less is known about DBMS…
Current operating systems are complex systems that were designed before today's computing environments. This makes it difficult for them to meet the scalability, heterogeneity, availability, and security challenges in current cloud and…
Massive Multi-Omics Microbiome Database (M3DB) is a data warehousing and analytics solution designed to handle diverse, complex, and unprecedented volumes of sequence and taxonomic classification data obtained in a typical microbiome…
Driven by the recent rapid increase in the number of materials databases published (open and commercial), I discuss here some perspectives on the growing need for standardized, interoperable, open databases. The field of computational…
We present DBNet, a data-driven network automation framework built on top of a DBMS. DBNet utilizes key primitives of a DBMS including tables, procedures, transactions, logging, and access control to serve the functions of a data-centric…
Databases are considered to be integral part of modern information systems. Almost every web or mobile application uses some kind of database. Database management systems are considered to be a crucial element from both business and…
Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI), particularly through the adoption of foundation models (FMs). These AI techniques have rapidly advanced, addressing historical…
In this paper we describe the support for data feed ingestion in AsterixDB, an open-source Big Data Management System (BDMS) that provides a platform for storage and analysis of large volumes of semi-structured data. Data feeds are a…
Recent trends in information management involve the periodic transcription of data onto secondary devices in a networked environment, and the proper scheduling of these transcriptions is critical for efficient data management. To assist in…
This article presents earlier results of our research works in the area of modeling Business Intelligence Systems. The basic idea of this research area is presented first. We then show the necessity of including certain users' parameters in…
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive…
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these…
The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical…
Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such…