486 research outputs found
SPARSITY HANDLING AND DATA EXPLOSION IN OLAP SYSTEMS
A common problem with OnLine Analytical Processing (OLAP) databases is data explosion - data size multiplies, when it is loaded from the source data into multidimensional cubes. Data explosion is not an issue for small databases, but can be serious problems with large databases. In this paper we discuss the sparsity and data explosion phenomenon in multidimensional data model, which lie at the core of OLAP systems. Our researches over five companies with different branch of business confirm the observations that in reality most of the cubes are extremely sparse. We also consider a different method that relational and multidimensional severs applies to reduce the data explosion and sparsity problems as compression and indexes techniques, partitioning, preliminary aggregations
Hierarchical Bin Buffering: Online Local Moments for Dynamic External Memory Arrays
Local moments are used for local regression, to compute statistical measures
such as sums, averages, and standard deviations, and to approximate probability
distributions. We consider the case where the data source is a very large I/O
array of size n and we want to compute the first N local moments, for some
constant N. Without precomputation, this requires O(n) time. We develop a
sequence of algorithms of increasing sophistication that use precomputation and
additional buffer space to speed up queries. The simpler algorithms partition
the I/O array into consecutive ranges called bins, and they are applicable not
only to local-moment queries, but also to algebraic queries (MAX, AVERAGE, SUM,
etc.). With N buffers of size sqrt{n}, time complexity drops to O(sqrt n). A
more sophisticated approach uses hierarchical buffering and has a logarithmic
time complexity (O(b log_b n)), when using N hierarchical buffers of size n/b.
Using Overlapped Bin Buffering, we show that only a single buffer is needed, as
with wavelet-based algorithms, but using much less storage. Applications exist
in multidimensional and statistical databases over massive data sets,
interactive image processing, and visualization
Automatic physical database design : recommending materialized views
This work discusses physical database design while focusing on the problem of selecting materialized views for improving the performance of a database system. We first address the satisfiability and implication problems for mixed arithmetic constraints. The results are used to support the construction of a search space for view selection problems. We proposed an approach for constructing a search space based on identifying maximum commonalities among queries and on rewriting queries using views. These commonalities are used to define candidate views for materialization from which an optimal or near-optimal set can be chosen as a solution to the view selection problem. Using a search space constructed this way, we address a specific instance of the view selection problem that aims at minimizing the view maintenance cost of multiple materialized views using multi-query optimization techniques. Further, we study this same problem in the context of a commercial database management system in the presence of memory and time restrictions. We also suggest a heuristic approach for maintaining the views while guaranteeing that the restrictions are satisfied. Finally, we consider a dynamic version of the view selection problem where the workload is a sequence of query and update statements. In this case, the views can be created (materialized) and dropped during the execution of the workload. We have implemented our approaches to the dynamic view selection problem and performed extensive experimental testing. Our experiments show that our approaches perform in most cases better than previous ones in terms of effectiveness and efficiency
Maintenance-cost view-selection in large data warehouse systems: algorithms, implementations and evaluations.
Choi Chi Hon.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 120-126).Abstracts in English and Chinese.Abstract --- p.iAbstract (Chinese) --- p.iiAcknowledgement --- p.iiiContents --- p.ivList of Figures --- p.viiiList of Tables --- p.xChapter 1 --- Introduction --- p.1Chapter 1.1 --- Maintenance Cost View Selection Problem --- p.2Chapter 1.2 --- Previous Research Works --- p.3Chapter 1.3 --- Major Contributions --- p.4Chapter 1.4 --- Thesis Organization --- p.6Chapter 2 --- Literature Review --- p.7Chapter 2.1 --- Data Warehouse and OLAP Systems --- p.8Chapter 2.1.1 --- What Is Data Warehouse? --- p.8Chapter 2.1.2 --- What Is OLAP? --- p.10Chapter 2.1.3 --- Difference Between Operational Database Systems and OLAP --- p.10Chapter 2.1.4 --- Data Warehouse Architecture --- p.12Chapter 2.1.5 --- Multidimensional Data Model --- p.13Chapter 2.1.6 --- Star Schema and Snowflake Schema --- p.15Chapter 2.1.7 --- Data Cube --- p.17Chapter 2.1.8 --- ROLAP and MOLAP --- p.19Chapter 2.1.9 --- Query Optimization --- p.20Chapter 2.2 --- Materialized View --- p.22Chapter 2.2.1 --- What Is A Materialized View --- p.23Chapter 2.2.2 --- The Role of Materialized View in OLAP --- p.23Chapter 2.2.3 --- The Challenges in Exploiting Materialized View --- p.24Chapter 2.2.4 --- What Is View Maintenance --- p.25Chapter 2.3 --- View Selection --- p.27Chapter 2.3.1 --- Selection Strategy --- p.27Chapter 2.4 --- Summary --- p.32Chapter 3 --- Problem Definition --- p.33Chapter 3.1 --- View Selection Under Constraint --- p.33Chapter 3.2 --- The Lattice Framework for Maintenance Cost View Selection Prob- lem --- p.35Chapter 3.3 --- The Difficulties of Maintenance Cost View Selection Problem --- p.39Chapter 3.4 --- Summary --- p.41Chapter 4 --- What Difference Heuristics Make --- p.43Chapter 4.1 --- Motivation --- p.44Chapter 4.2 --- Example --- p.46Chapter 4.3 --- Existing Algorithms --- p.49Chapter 4.3.1 --- A*-Heuristic --- p.51Chapter 4.3.2 --- Inverted-Tree Greedy --- p.52Chapter 4.3.3 --- Two-Phase Greedy --- p.54Chapter 4.3.4 --- Integrated Greedy --- p.57Chapter 4.4 --- A Performance Study --- p.60Chapter 4.5 --- Summary --- p.68Chapter 5 --- Materialized View Selection as Constrained Evolutionary Opti- mization --- p.71Chapter 5.1 --- Motivation --- p.72Chapter 5.2 --- Evolutionary Algorithms --- p.73Chapter 5.2.1 --- Constraint Handling: Penalty v.s. Stochastic Ranking --- p.74Chapter 5.2.2 --- The New Stochastic Ranking Evolutionary Algorithm --- p.78Chapter 5.3 --- Experimental Studies --- p.81Chapter 5.3.1 --- Experimental Setup --- p.82Chapter 5.3.2 --- Experimental Results --- p.82Chapter 5.4 --- Summary --- p.89Chapter 6 --- Dynamic Materialized View Management Based On Predicates --- p.90Chapter 6.1 --- Motivation --- p.91Chapter 6.2 --- Examples --- p.93Chapter 6.3 --- Related Work: Static Prepartitioning-Based Materialized View Management --- p.96Chapter 6.4 --- A New Dynamic Predicate-based Partitioning Approach --- p.99Chapter 6.4.1 --- System Overview --- p.102Chapter 6.4.2 --- Partition Advisor --- p.103Chapter 6.4.3 --- View Manager --- p.104Chapter 6.5 --- A Performance Study --- p.108Chapter 6.5.1 --- Performance Metrics --- p.110Chapter 6.5.2 --- Feasibility Studies --- p.110Chapter 6.5.3 --- Query Locality --- p.112Chapter 6.5.4 --- The Effectiveness of Disk Size --- p.115Chapter 6.5.5 --- Scalability --- p.115Chapter 6.6 --- Summary --- p.116Chapter 7 --- Conclusions and Future Work --- p.118Bibliography --- p.12
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
RDF Analytics: Lenses over Semantic Graphs
International audienceThe development of Semantic Web (RDF) brings new requirements for data analytics tools and methods, going beyond querying to semantics-rich analytics through warehouse-style tools. In this work, we fully redesign, from the bottom up, core data analytics concepts and tools in the context of RDF data, leading to the first complete formal framework for warehouse-style RDF analytics. Notably, we define i) analytical schemas tailored to heterogeneous, semantics-rich RDF graph, ii) analytical queries which (beyond relational cubes) allow flexible querying of the data and the schema as well as powerful aggregation and iii) OLAP-style operations. Experiments on a fully-implemented platform demonstrate the practical interest of our approach
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