Sanjay Goil

HAVEn is HP's Big Data Platform. Success of the platform depends on the applications developed on it. This paper describes the HAVEn workbench, a cross-engine layer that provides out-of-box ability to create new analytics applications by application developers and data scientists, hosted on the cloud ( SaaS).

HPC programmers have traditionally been able to use all the
compute power made available to them. Even with the performance
leaps that Moore’s law has allowed Intel architecture to deliver over
the past decade, the hunger for additional performance continues to
thrive. There are big unsolved problems in science and engineering,
physical simulations at higher granularities, and problems where the
economically viable compute power provides lower resolution or
piecemeal… Mehr zeigen

HPC programmers have traditionally been able to use all the
compute power made available to them. Even with the performance
leaps that Moore’s law has allowed Intel architecture to deliver over
the past decade, the hunger for additional performance continues to
thrive. There are big unsolved problems in science and engineering,
physical simulations at higher granularities, and problems where the
economically viable compute power provides lower resolution or
piecemeal simulation of smaller portions of the larger problem.
This is what makes serving the HPC market so exciting for Intel, and
it is a significant driver for innovation in both hardware and software
methodologies for parallelism and performance. Weniger anzeigen

In this paper we present a scalable parallel subspace clustering algorithm which
has both data and task parallelism embedded in it. We also formulate the
technique of adaptive grids and present a truly un-supervised clustering algorithm
requiring no user inputs. Our implementation shows near linear
speedups with negligible communication overheads. The use of adaptive
grids results in two orders of magnitude improvement in the computation
time of our serial algorithm over… Mehr zeigen

In this paper we present a scalable parallel subspace clustering algorithm which
has both data and task parallelism embedded in it. We also formulate the
technique of adaptive grids and present a truly un-supervised clustering algorithm
requiring no user inputs. Our implementation shows near linear
speedups with negligible communication overheads. The use of adaptive
grids results in two orders of magnitude improvement in the computation
time of our serial algorithm over current methods with much better quality
of clustering. Weniger anzeigen

Decision support systems are important in leveraging information present in data warehouses in businesses like banking, insurance, retail and health-care among many others. The multi-dimensional aspects of a business can be naturally expressed using a multi-dimensional data model. Data analysis and data mining on these warehouses pose new challenges for traditional database systems. OLAP and data mining operations require summary information on these multi-dimensional data sets. Query… Mehr zeigen

Decision support systems are important in leveraging information present in data warehouses in businesses like banking, insurance, retail and health-care among many others. The multi-dimensional aspects of a business can be naturally expressed using a multi-dimensional data model. Data analysis and data mining on these warehouses pose new challenges for traditional database systems. OLAP and data mining operations require summary information on these multi-dimensional data sets. Query processing for these applications require different views of data for analysis and effective decision making. Data mining techniques can be applied in conjunction with OLAP for an integrated business solution. As data warehouses grow, parallel processing techniques have been applied to enable the use of larger data sets and reduce the time for analysis, thereby enabling evaluation of many more options for decision making. Weniger anzeigen