cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes

Chen, Y.; Rau-Chaplin, Andrew; Dehne, Frank; Eavis, Todd; Green, D.; Sithirasenan, Elankayer

doi:10.1109/icde.2006.32

Cited by 11 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coordinator is responsible for processing TPC-R SQL workload. • cgmOLAP Chen et al [22] claim that cgmOLAP is the first fully functional parallel OLAP system able to build data cubes at a rate of more than 1 Terabyte per hour. In this Section, we provide our theoretical results in the context of data warehouse fragmentation for supporting optimal building of OLAP data cubes.…”

Section: Related Workmentioning

confidence: 99%

A Cloud-Based Framework for Supporting Effective and Efficient OLAP in Big Data Environments

Cuzzocrea

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

View full text Add to dashboard Cite

Inspired by the emerging Big Data challenge, in this paper we provide the description of OLAP*, a Cloudbased framework for supporting effective and efficient OLAP in Big Data environments. OLAP* combines data warehouse partitioning techniques with Cloud Computing paradigms, and provides a suitable implementation on top of the well-known ROLAP server Mondrian where the main task consists in applying meaningful transformation of multidimensional database schemas. We complement our analytical contribution by mean of a case study showing the effectiveness of our framework in a practical setting.

show abstract

Section: Related Workmentioning

confidence: 99%

A Cloud-Based Framework for Supporting Effective and Efficient OLAP in Big Data Environments

Cuzzocrea

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

View full text Add to dashboard Cite

show abstract

“…As a result of these combined factors, DVE applications place considerable and unpredictable pressure on both network and server-side data processing resources. Fortunately, the database and visualization communities have made considerable progress in reducing server-side [49, 86,45,14,81] and client-side [29,45] data processing and rendering latencies. However, network bottlenecks still persist, and can cripple user-facing responsiveness even if server and client-side overheads are eliminated.…”

Section: Introductionmentioning

confidence: 99%

Continuous Prefetch for Interactive Data Applications

Mohammed

2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

Interactive data visualization and exploration (DVE) applications are often network-bottlenecked due to bursty request patterns, large response sizes, and heterogeneous deployments over a range of networks and devices. This makes it difficult to ensure consistently low response times (< 100ms). Khameleon is a framework for DVE applications that uses a novel combination of prefetching and response tuning to dynamically trade-off response quality for low latency. Khameleon exploits DVE's approximation tolerance: immediate lower-quality responses are preferable to waiting for complete results. To this end, Khameleon progressively encodes responses, and runs a server-side scheduler that proactively streams portions of responses using available bandwidth to maximize user-perceived interactivity. The scheduler involves a complex optimization based on available resources, predicted user interactions, and response quality levels; yet, decisions must also be made in real-time. To overcome this, Khameleon uses a fast greedy heuristic that closely approximates the optimal approach. Using image exploration and visualization applications with real user interaction traces, we show that across a wide range of network and client resource conditions, Khameleon outperforms existing prefetching approaches that benefit from perfect prediction models: Khameleon always lowers response latencies (typically by 2-3 orders of magnitude) while keeping response quality within 50-80%.

show abstract

“…Our contribution is towards improving interactive exploration in a session context. Cube Materialization: Materialization strategies range from full-cube materialization over MapReduce [37] to regionspecific materialization [11] to selective partial materialization. Optimization techniques exist for optimizing intra-query parallelization [3], but do not consider multiple queries as part of an interactive session.…”

Section: Related Workmentioning

confidence: 99%

Distributed and interactive cube exploration

Kamat

Jayachandran

Tunga

et al. 2014

2014 IEEE 30th International Conference on Data Engineering

111

View full text Add to dashboard Cite

Abstract-Interactive ad-hoc analytics over large datasets has become an increasingly popular use case. We detail the challenges encountered when building a distributed system that allows the interactive exploration of a data cube. We introduce DICE, a distributed system that uses a novel session-oriented model for data cube exploration, designed to provide the user with interactive sub-second latencies for specified accuracy levels. A novel framework is provided that combines three concepts: faceted exploration of data cubes, speculative execution of queries and query execution over subsets of data. We discuss design considerations, implementation details and optimizations of our system. Experiments demonstrate that DICE provides a subsecond interactive cube exploration experience at the billion-tuple scale that is at least 33% faster than current approaches.

show abstract

cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes

Cited by 11 publications

References 9 publications

A Cloud-Based Framework for Supporting Effective and Efficient OLAP in Big Data Environments

A Cloud-Based Framework for Supporting Effective and Efficient OLAP in Big Data Environments

Continuous Prefetch for Interactive Data Applications

Distributed and interactive cube exploration

Contact Info

Product

Resources

About