Inference control in Online Analytical Processing (OLAP) systems is employed to protect sensitive data from being inferred while, at the same time, ensuring that legitimate requests can be consistently satisfied. Many models have been proposed, however most of them are suitable for only one type of aggregation, others adopted a detect-and-remove approach, which typically requires complex computations over the data and is thus too expensive to be applied in OLAP systems. In this paper, we present a practical inference control model for protecting OLAP cubes against inference attacks. PICM's has a more general framework; it is applied to any aggregation functions. In addition, PICM eliminates the source of the inference instead of detecting them. This gives a great advantage that the inference checking can, in fact, be carried out without a meaningful impact upon final query execution times.

Online analytical processing OLAP is considered as a database prototype that gives a platform for the rich analysis of multidimensional data. A logical data structure known as the data cube often supports the OLAP. However, mining association rules from multidimensional data using OLAP techniques with data mining facilities is an issue of substantial complexity. In practice, the complexity is excited by the existence of dimension hierarchies that subdivide dimensions into aggregation layers of various granularity. Discovery of hierarchy-sensitive association rules can be very costly on large cubes. In this paper, we present an OLAP hierarchy-sensitive framework that supports the efficient and transparent manipulation of dimension hierarchies for extracting association rules from data cube. The experimental results show that, when compared to the alternatives, very slight overhead is required to handle streams of inter-dimensional association rules requests.

Contemporary Online Analytical Processing systems often make extensive use of view materialization in order to minimize the cost of run-time query processing on massive data sets. Due to the cost of generation and maintenance, however, such views have typically been updated only in periodic batches. That being said, users of Big Data analytics platforms are demanding an increasingly shorter update cycle. In this paper, we present a framework for what we call soft real-time OLAP. Specifically, we employ view partitioning and maintain an additional "hot" partition that absorbs incoming update streams. We then augment this strategy with multi-core processing so as to further accelerate view construction and query resolution. Initial experiments with input sets of up to 10 million tuples show that our framework improves update performance by almost two orders of magnitude, while significantly reducing view construction and query costs.

Security policies in Online Analytical Processing (OLAP) systems are designed to protect sensitive data from unauthorized access while, at the same time, ensuring that legitimate requests can be consistently satisfied. Ultimately, such policies allow administrators to define a series of restrictions and/or exceptions that can be associated with the components of the OLAP data model, including elements such as dimensions, cells, and aggregation hierarchies. A primary limitation of many current systems is that security policies are generally constructed on top of very granular privilege models that can produce complex and error prone mappings to the elements of the OLAP domain. In this paper, we present an Object Oriented Security Model (OSSM) that has been specifically designed for the specification of security policies within OLAP environments. In addition to explicit support for components of the conceptual data model, the OSSM can be used by the associated security policy engine to transparently and consistently propagate constraints across all relevant levels of dimension hierarchies. We discuss the core elements of the OSSM, as well as the integration with the policy engine that supports the language interfaces.

Over the past generation, data warehousing and online analytical processing (OLAP) applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional relational DBMSs (ROLAP). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP’s table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems. In this paper, we describe a storage and indexing framework that aims to provide both MOLAP like performance and ROLAP like scalability by essentially combining some of the best features from both. Based upon a combination of R-trees and bitmap indexes, the storage engine has been integrated with a robust OLAP query engine prototype that is able to fully exploit the efficiency of the proposed storage model. Specifically, it utilizes an OLAP algebra coupled with a domain specific query optimizer, to map user queries directly to the storage and indexing framework. Experimental results demonstrate that not only does the design improve upon more naive approaches, but that it does indeed offer the potential to optimize both query performance and scalability.

A central promise of cloud services is elastic, on-demand provisioning. The provisioning of data on temporarily available nodes is what makes elastic database services a hard problem. The essential task that enables elastic data services is bringing a node and its data up-to-date. Strategies for high availability do not satisfy the need in this context because they bring nodes online and up-to-date by repeating history, e.g., by log shipping. Nodes must become up-to-date and useful for query processing incrementally by key range. What is wanted is a technique such that in a newly added node, during each short period of time, an additional small key range becomes up-to-date, until eventually the entire dataset becomes up-to-date and useful for query processing, with overall update performance comparable to a traditional high-availability strategy that carries the entire dataset forward without regard to key ranges. Even without the entire dataset being available, the node is productive and participates in query processing tasks. The authors' proposed solution relies on techniques from partitioned B-trees, adaptive merging, deferred maintenance of secondary indexes and of materialized views, and query optimization using materialized views. The paper introduces a family of maintenance strategies for temporarily available copies, the space of possible query execution plans and their cost functions, as well as appropriate query optimization techniques.

—Online Analytical Processing (OLAP) has become an increasingly important and prevalent component of Decision Support Systems. OLAP is associated with a data model known as a cube, a multi-dimensional representation of the core measures and relationships within the associated organization. While numerous cube generation and processing algorithms have been presented in the literature, little effort has been made to address the unique security and authorization requirements of the model. In particular, the hierarchical nature of the cube allows users to bypass - either intentionally or unintentionally - partial constraints defined at alternate aggregation levels. In this paper, we present an authorization framework that builds upon an algebra designed specifically for OLAP domains. It is Object-Oriented in nature and uses query re-writing rules to ensure consistent data access across all levels of the conceptual model. For the most part, the process is largely transparent to the user. We demonstrate the scope of our framework with a series of common OLAP query case studies, as well as an experimental performance analysis using a common OLAP benchmark. The end result is an intuitive but powerful approach to database authorization that is uniquely tailored to the OLAP domain.

Current OLAP servers are typically implemented as either extensions to conventional relational databases or as non-relational array-based storage engines. In the former case, the unique modeling and processing requirements of OLAP systems often make for a relatively awkward fit with RDBM systems. In the latter case, the proprietary nature of the MOLAP implementations has largely prevented the emergence of a standardized query model. In this paper, we discuss an algebra for the specification, optimization, and execution of OLAP-specific queries, including its ability to support a native language query framework. In addition, we ground the conceptual work by incorporating the query optimization and execution facilities into a fully functional OLAP-aware DBMS prototype. Experimental results clearly demonstrate the potential of the new algebra-driven system relative to both the un-optimized prototype and a pair of popular enterprise servers.

Over the past generation, data warehousing and OLAP applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional relational DBMSs (ROLAP). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP’s table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems. In this paper, we describe a storage and indexing framework that aims to provide both MOLAP like performance and ROLAP like scalability by essentially combining some of the best features of both. Based upon a combination of R-trees and bitmap indexes, the storage engine has been integrated with a robust OLAP query engine prototype that is able to fully exploit the efficiency of the proposed storage model. Experimental results demonstrate that not only does the framework improve upon more naive approaches, but that it does indeed offer the potential to optimize both query performance and scalability.

Over the past 15 years, data warehousing and OLAP technologies have matured to the point whereby they have become a cornerstone for the decision making process in organizations of all sizes. With the underlying databases growing enormously in size, parallel DBM systems have become a popular target platform. Perhaps the most ``obvious'' approach to scalable warehousing is to combine a small collection of conventional relational DBMSs into a loosely connected parallel DBMS. Such systems, however, benefit little, if at all, from advances in OLAP indexing, storage, compression, modeling, or query optimization. In the current paper, we discuss a parallel analytics server that has been designed from the ground up as a high performance OLAP query engine. Moreover, its indexing and query processing model directly exploits an OLAP-specific algebra that enables performance optimizations beyond the reach of simple relational DBMS clusters. Taken together, the server provides class-leading query performance with the scalability of shared nothing databases and, perhaps most importantly, achieves this balance with a modest physical architecture.

In multi-dimensional database environments, such as those typically associated with contemporary data warehousing, we generally require effective indexing mechanisms for all but the smallest data sets. While numerous such methods have been proposed, the R-tree has emerged as one of the most common and reliable indexing models. Nevertheless, as user queries grow in terms of both size and dimensionality, R-tree performance can deteriorate significantly. Moreover, in the multi-terabyte spaces of today’s enterprise warehouses, the combination of data and indexes ? R-tree or otherwise ? can produce unacceptably large storage requirements. In this chapter, the authors present a framework that addresses both of these concerns. First, they propose a variation of the classic R-tree that specifically targets data warehousing architectures. Their new LBF R-tree not only improves performance on common user-defined range queries, but gracefully degrades to a linear scan of the data on pathologically large queries. Experimental results demonstrate a reduction in disk seeks of more than 50% relative to more conventional R-tree designs. Second, the authors present a fully integrated, block-oriented compression model that reduces the storage footprint of both data and indexes. It does so by exploiting the same Hilbert space filling curve that is used to construct the LBF R-tree itself. Extensive testing demonstrates compression rates of more than 90% for multi-dimensional data, and up to 98% for the associated indexes.

Over the past ten to fifteen years Business Intelligence applications have become increasingly important and visible components of enterprize computing environments While relational database management systems often form the backbone of the BI software stack the unique modeling and processing requirements of BI applications often make for a relatively awkward fit with RDBMS platforms in general and their SQL query interfaces in particular In this paper, we present a new framework for BI/OLAP applications that directly exploits a domain specific conceptual data model In turn the new paradigm allows us to support native client side OOP querying without the need to embed an intermediate non OOP language such as SQL or MDX A pre processor essentially translates standard OOP source code into a query grammar developed specifically for BI analysis The end result is a query facility that is far more intuitive to use as well as being more amenable to con temporary code development tools We provide numerous examples to illustrate the flexibility and convenience of the new framework