Related papers: Version 0.1 of the BigDAWG Polystore System
Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are…
The widespread development and adoption of open-source software have built an ecosystem for open development and collaboration. In this ecosystem, individuals and organizations collaborate to create high-quality software that can be used by…
Scientific applications produce a huge amount of data, which imposes serious management and analysis challenges. In particular, limitations in current database management systems prevent their adoption in simulation applications, in which…
AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data…
Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed…
Big data management aims to establish data hubs that support data in multiple models and types in an all-around way. Thus, the multi-model database system is a promising architecture for building such a multi-model data store. For an…
The relational DBMS (RDBMS) has been widely used since it supports various high-level functionalities such as SQL, schemas, indexes, and transactions that do not exist in the O/S file system. But, a recent advent of big data technology…
NoSQL databases are widely used for massive data storage and real-time web applications. Yet important aspects of these data structures are not well understood. For example, NoSQL databases write most of their data to a collection of files…
Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different…
Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear, however, how well these…
Many fields of science rely on relational database management systems to analyze, publish and share data. Since RDBMS are originally designed for, and their development directions are primarily driven by, business use cases they often lack…
The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity…
Current generation of Internet-based services are typically hosted on large data centers that take the form of warehouse-size structures housing tens of thousands of servers. Continued availability of a modern data center is the result of a…
With the rapid advancement of brain-computer interface (BCI) technology, the volume of physiological data generated in related research and applications has grown significantly. Data is a critical resource in BCI research and a key factor…
Database Management System (DBMS) is designed to help store and process large collections of data, and is incredibly flexible to perform various kinds of optimizations as long as it achieves serializability with a high-level interface…
Data distribution across different facilities offers benefits such as enhanced resource utilization, increased resilience through replication, and improved performance by processing data near its source. However, managing such data is…
One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources. Since data warehouses have been used for several decades to achieve the same goal, they could be leveraged also to provide…
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…
In recent years, resource elasticity and cost optimization have become essential for RDBMSs. While cloud-native RDBMSs provide elastic computing resources via disaggregated computing and storage, storage costs remain a critical user…
Analyzing large scale data has emerged as an important activity for many organizations in the past few years. This large scale data analysis is facilitated by the MapReduce programming and execution model and its implementations, most…