Related papers: Building an Effective Data Warehousing for Financi…

Configuring a storage system to better serve an application is a challenging task complicated by a multidimensional, discrete configuration space and the high cost of space exploration (e.g., by running the application with different…

A common task in scientific computing is the derivation of data. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for…

A data warehouse is a large data repository for the purpose of analysis and decision making in organizations. To improve the query performance and to get fast access to the data, data is stored as materialized views (MV) in the data…

The paper demonstrates the Windows data collectordevelopment process with the built back-end from the require-ments gathering stage till the implementation and testing phase.Each phase throughout the development life cycle of the systemis…

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets…

E-commerce with major online retailers is changing the way people consume. The goal of increasing delivery speed while remaining cost-effective poses significant new challenges for supply chains as they race to satisfy the growing and…

Temporal data are ubiquitous in the financial services (FS) industry -- traditional data like economic indicators, operational data such as bank account transactions, and modern data sources like website clickstreams -- all of these occur…

A number of data acquisition systems depend on human interface to access computer for measuring, processing and analyzing data and to prepare it for presentation and storage. Data acquisition software is installed on the computer and all…

Workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on…

The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this…

Modern hardware heterogeneity brings efficiency and performance opportunities for analytical query processing. In the presence of continuous data volume and complexity growth, bridging the gap between recent hardware advancements and the…

Data visualization is the process by which data of any size or dimensionality is processed to produce an understandable set of data in a lower dimensionality, allowing it to be manipulated and understood more easily by people. The goal of…

With the advent of the Internet-of-Things (IoT), handling large volumes of time-series data has become a growing concern. Data, generated from millions of Internet-connected sensors, will drive new IoT applications and services. A key…

We propose, implement, and experimentally evaluate a runtime middleware to support high-throughput execution on hybrid cluster machines of large-scale analysis applications. A hybrid cluster machine consists of computation nodes which have…

Modern science and engineering computing environments often feature storage systems of different types, from parallel file systems in high-performance computing centers to object stores operated by cloud providers. To enable easy, reliable,…

The popularity of business intelligence (BI) systems to support business analytics has tremendously increased in the last decade. The determination of data items that should be stored in the BI system is vital to ensure the success of an…

Data pre-processing pipelines are the bread and butter of any successful AI project. We introduce a novel programming model for pipelines in a data lakehouse, allowing users to interact declaratively with assets in object storage. Motivated…

Cloud computing is widely adopted by corporate as well as retail customers to reduce the upfront cost of establishing computing infrastructure. However, switching to the cloud based services poses a multitude of questions, both for…

Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is a solution that is inflexible, incurs in high costs, leads to "silos" that prevent sharing data…

Improving the performance and reducing the cost of cloud data systems is increasingly challenging. Data processing units (DPUs) are a promising solution, but utilizing them for data processing needs characterizing the new hardware and…