scholarly journals Dependent types for enforcement of information flow and erasure policies in heterogeneous data structures

Author(s):  
Gordon Stewart ◽  
Anindya Banerjee ◽  
Aleksandar Nanevski
2011 ◽  
pp. 211-236 ◽  
Author(s):  
Shastri L. Nimmagadda ◽  
Heinz Dreher

Several issues of database organization of petroleum industries have been highlighted. Complex geo-spatial heterogeneous data structures complicate the accessibility and presentation of data in petroleum industries. Objectives of the current research are to integrate the data from different sources and connecting them intelligently. Data warehousing approach supported by ontology, has been described for effective data mining of petroleum data sources. Petroleum ontology framework, narrating the conceptualization of petroleum ontology and methodological architectural views, has been described. Ontology based data warehousing with fine-grained multidimensional data structures, facilitate to mining and visualization of data patterns, trends, and correlations, hidden under massive volumes of data. Data structural designs and implementations deduced, through ontology supportive data warehousing approaches, will enable the researchers in commercial organizations, such as, the one of Western Australian petroleum industries, for knowledge mapping and thus interpret knowledge models for making million dollar financial decisions.


2008 ◽  
pp. 1901-1925 ◽  
Author(s):  
Shastri L. Nimmagadda ◽  
Heinz Dreher

Several issues of database organization of petroleum industries have been highlighted. Complex geo-spatial heterogeneous data structures complicate the accessibility and presentation of data in petroleum industries. Objectives of the current research are to integrate the data from different sources and connecting them intelligently. Data warehousing approach supported by ontology, has been described for effective data mining of petroleum data sources. Petroleum ontology framework, narrating the conceptualization of petroleum ontology and methodological architectural views, has been described. Ontology based data warehousing with fine-grained multidimensional data structures, facilitate to mining and visualization of data patterns, trends, and correlations, hidden under massive volumes of data. Data structural designs and implementations deduced, through ontology supportive data warehousing approaches, will enable the researchers in commercial organizations, such as, the one of Western Australian petroleum industries, for knowledge mapping and thus interpret knowledge models for making million dollar financial decisions.


Author(s):  
Thomas Moser ◽  
Stefan Biffl ◽  
Wikan Danar Sunindyo ◽  
Dietmar Winkler

The engineering of a complex production automation system involves experts from several backgrounds, such as mechanical, electrical, and software engineering. The production automation expert knowledge is embedded in their tools and data models, which are, unfortunately, insufficiently integrated across the expert disciplines, due to semantically heterogeneous data structures and terminologies. Traditional integration approaches to data integration using a common repository are limited as they require an agreement on a common data schema by all project stakeholders. This paper introduces the Engineering Knowledge Base (EKB), a semantic-web-based framework, which supports the efficient integration of information originating from different expert domains without a complete common data schema. The authors evaluate the proposed approach with data from real-world use cases from the production automation domain on data exchange between tools and model checking across tools. Major results are that the EKB framework supports stronger semantic mapping mechanisms than a common repository and is more efficient if data definitions evolve frequently.


2021 ◽  
Vol 5 (ICFP) ◽  
pp. 1-30
Author(s):  
Joshua Yanovski ◽  
Hoang-Hai Dang ◽  
Ralf Jung ◽  
Derek Dreyer

The Rust language offers a promising approach to safe systems programming based on the principle of aliasing XOR mutability : a value may be either aliased or mutable, but not both at the same time. However, to implement pointer-based data structures with internal sharing, such as graphs or doubly-linked lists, we need to be able to mutate aliased state. To support such data structures, Rust provides a number of APIs that offer so-called interior mutability : the ability to mutate data via method calls on a shared reference. Unfortunately, the existing APIs sacrifice flexibility, concurrent access, and/or performance, in exchange for safety. In this paper, we propose a new Rust API called GhostCell which avoids such sacrifices by separating permissions from data : it enables the user to safely synchronize access to a collection of data via a single permission. GhostCell repurposes an old trick from typed functional programming: branded types (as exemplified by Haskell’s ST monad), which combine phantom types and rank-2 polymorphism to simulate a lightweight form of state-dependent types. We have formally proven the soundness of GhostCell by adapting and extending RustBelt, a semantic soundness proof for a representative subset of Rust, mechanized in Coq.


Author(s):  
Ranjit Biswas

The homogeneous data structure ‘train' and the heterogeneous data structure ‘atrain' are the fundamental, very powerful dynamic and flexible data structures, being the first data structures introduced exclusively for big data. Thus ‘Data Structures for Big Data' is to be regarded as a new subject in Big Data Science, not just as a new topic, considering the explosive momentum of the big data. Based upon the notion of the big data structures train and atrain, the author introduces the useful data structures for the programmers working with big data which are: homogeneous stacks ‘train stack' and ‘rT-coach stack', heterogeneous stacks ‘atrain stack' and ‘rA-coach stack', homogeneous queues ‘train queue' and ‘rT-coach queue', heterogeneous queues ‘atrain queue' and ‘rA-coach queue', homogeneous binary trees ‘train binary tree' and ‘rT-coach binary tree', heterogeneous binary trees ‘atrain binary tree' and ‘rA-coach binary tree', homogeneous trees ‘train tree' and ‘rT-coach tree', heterogeneous trees ‘atrain tree' and ‘rA-coach tree', to enrich the subject ‘Data Structures for Big Data' for big data science.


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