Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby
massive amounts of heterogeneous data are gathered from multiple sources, managed …

In many organizations, it is often challenging for users to find relevant data for specific tasks,
since the data is usually scattered across the enterprise and often inconsistent. In fact, data …

Protected database search systems cryptographically isolate the roles of reading from,
writing to, and administering the database. This separation limits unnecessary administrator …

Data federation addresses the problem of uniformly accessing multiple, possibly
heterogeneous data sources, by map** them into a unified schema, such as an RDF …

Modern applications often need to manage and analyze widely diverse datasets that span
multiple data models [1],[2],[3],[4],[5]. Warehousing the data through Extract-Transform-Load …

In this paper, we argue that in many “Big Data” applications, getting data into the system
correctly and at scale via traditional ETL (Extract, Transform, and Load) processes is a …

Organizations are often faced with the challenge of providing data management solutions for
large, heterogenous datasets that may have different underlying data and programming …

In this paper we present BatchDB, an in-memory database engine designed for hybrid OLTP
and OLAP workloads. BatchDB achieves good performance, provides a high level of data …

The concept of state and its applications vary widely across big data processing systems.
This is evident in both the research literature and existing systems, such as Apache Flink …

Big Data is used in decision making process to gain useful insights hidden in the data for
business and engineering. At the same time it presents challenges in processing, cloud …