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Über dieses Buch

This book presents Hyper-lattice, a new algebraic model for partially ordered sets, and an alternative to lattice. The authors analyze some of the shortcomings of conventional lattice structure and propose a novel algebraic structure in the form of Hyper-lattice to overcome problems with lattice. They establish how Hyper-lattice supports dynamic insertion of elements in a partial order set with a partial hierarchy between the set members. The authors present the characteristics and the different properties, showing how propositions and lemmas formalize Hyper-lattice as a new algebraic structure.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Hyper-lattice

In this chapter we introduce and discuss the notion of hyperlattice, a generalization of the notion of lattice of cuboidsLattice of Cuboids which is at the basis of most data warehousing techniques.

Soumya Sen, Agostino Cortesi, Nabendu Chaki

Chapter 2. Applications of Hyper-lattice in Real Life

In this chapter, we illustrate different business and real life situations where Hyper-lattice increases efficiency in data analytics. In Sect. 2.2, different case studies are considered on five different application environments. Within a short space, we have considered variations common in data warehouses and how to use Hyper Lattice to represent, and model such situations.

Soumya Sen, Agostino Cortesi, Nabendu Chaki

Chapter 3. Generating Co-operative Queries Over Concept Hierarchies

In this chapter, we discuss how using co-operative query languageCo-operative query languages can be used to increase efficiency of analytics when it’s used on Concept Hierarchies. Concept HierarchyConcept hierarchy presents the information of a same dimension in different abstracted levels. This abstraction allows us to identify the same data in multiple granularities and from different users’ perspectives. Conventional query execution retrieves information in one abstracted form only for the given dimension. Actually traditional database management models including RDBMS do not store the concept hierarchy information. This would be more relevant for online analytic processing (OLAP) on data warehouse. Indeed, it is a challenge for designer of data analytics application software to use a query language to take the benefit of concept hierarchy towards extracting optimized information for specific users. In the rest of the chapter, we have explored cooperative query language in this context and establish its suitability.

Soumya Sen, Agostino Cortesi, Nabendu Chaki

Chapter 4. Conclusions

The primary motivation of this book is to introduce Hyper-lattice as a new algebraic structure and to establish the context where this handles multi-dimensional data archive in a way which is more flexible and comprehensive as compared to conventional Lattice structures. Thus, Hyper-lattice can be used as a model multi-dimensional data to be stored and accessed for online data analytics.

Soumya Sen, Agostino Cortesi, Nabendu Chaki

Backmatter

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