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ETL (Extract, Transform and Loading) is a data warehousing process that migrate the data from source database by performing certain transformation rules over the extracted data. This transformed data is loaded back to the target database. With emergence of Big Data various organization are moving toward big data technology like Hadoop, Hadoop Ecosystem Projects like Hive and HBase to store their data...
The CloudMdsQL polystore provides integrated access to multiple heterogeneous data stores, such as RDBMS, NoSQL or even HDFS through a big data analytics framework such as MapReduce or Spark. The CloudMdsQL language is a functional SQL-like query language with a flexible nested data model. A major capability is to exploit the full power of each of the underlying data stores by allowing native queries...
Most data scientists use nowadays functional or semi-functional languages like SQL, Scala or R to treat data, obtained directly from databases. Such process requires to fetch data, process it, then store again, and such process tends to be done outside the DB, in often complex data-flows. Recently, database service providers have decided to integrate "R-as-a-Service" in their DB solutions...
Big Data refers to data volumes in the range of Exabyte (1018) and beyond. Such volumes exceed the capacity of current on-line storage and processing systems. With characteristics like volume, velocity and variety big data throws challenges to the traditional IT establishments. Computer assisted innovation, real time data analytics, customer-centric business intelligence, industry wide decision making...
Online Analytics Processing (OLAP) is utilised to develop multidimensional operations enabling queries and visualisation for Business Intelligence (BI). Most of the OLAP systems come with a tightly integrated user interface for querying and visualisation of data without the core OLAP operations exposed as an API. Advanced BI applications can be developed and composed to create complex workflows if...
Providers (SP) are wishing to increase their Return of Investment (ROI) by utilizing the data assets generated by tracking subscriber behaviors. This results in the ability of applying personalized policies, monitoring and controlling the service traffic to subscribers and gaining more revenues through the usage of subscriber data with ad networks. In this paper, a framework is developed to monitor...
Cloud services are widely used across the globe to store and analyze Big Data. These days it seems the news is full of stories about security breaches to these services, resulting in the exposure of huge amounts of private data. This paper studies the current security threats to Cloud Services, Big Data, and Hadoop. The paper analyzes a newly proposed Big Data security system based on the EnCoRe system...
Array Databases close a gap in the database ecosystem by adding modeling, storage, and processing support on multi-dimensional arrays. Declarative queries provide processing of arrays of regularly massive size, such as Tera-to Petabyte datacubes, while allowing internal degrees of freedom in partitioning the large arrays into tractable sub-arrays. Among the important new operations is the array Theta-Join,...
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 and variety. Numerous tools exist that allow users to store, query and index these massive quantities of data. Each storage or database engine comes with the promise...
Novel sensor-equipped smartphones have enabled the possibility of harvesting large quantities of data in urban areas by opportunistically involving citizens and their portable devices, as mobile sensors widely available and distributed over Smart Cities areas, typically defined as Mobile Crowd Sensing (MCS). Although some existing efforts have already tackled some of the several MCS issues, to the...
Recent advances in sensor-equipped smartphones are opening brand new opportunities, such as automatically extracting Points Of Interest (POIs) and mobility habits of citizens in Smart Cities from the large amount of harvested data hotspots. At the same time, the high dynamicity and unpredictability of Smart Cities crowds, opportunistically collaborating toward these common crowdsensing tasks, introduces...
Data loading is a crucial and well standardized procedure of the usage of modern relational database management systems (RDBMS). If data arises highly frequently and intensive real time analyses are required, single rows need to be imported as soon as they are generated, e.g., By monitoring applications, into a columnar table layout. The experiment described and conducted in this paper, evaluates...
Nowadays, the Internet Service Providers have to keep track of and in some cases to analyze for legal issues, a great amount of Internet data. Real-time big data processing and analysis introduce new challenges that must be addressed by system engineers. This is because: 1) traditional technologies exploiting databases are not designed to process a huge amount of data in real-time 2) classic machine...
Analytics-as-a-Service (AaaS) has become indispensable because it affords stakeholders to discover knowledge in Big Data. Previously, data stored in data warehouses follow some schema and standardization which leads to efficient data mining. However, the "Big Data" epoch has witnessed the rise of structured, semi-structured, and unstructured data, a trend that motivated enterprises to employ...
NoSQL databases are growing in popularity for Big Data applications in web analytics and supporting large web sites due to their high availability and scalability. Since each NoSQL system has its own API and does not typically support standards such as SQL and JDBC, integrating these systems with other enterprise and reporting software requires extra effort. In this work, we present a generic standards-based...
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