Cyber maturity in the Asia-Pacific Region 2014

Summary: To make considered, evidence-based cyber policy judgements in the Asia-Pacific there’s a need for better tools to assess the existing ‘cyber maturity’ of nations in the region. Over the past twelve months the Australian Strategic Policy Institute’s International Cyber Policy Centre has developed a Maturity Metric which provides an assessment of the regional cyber landscape. This measurement encompasses an evaluation of whole-of-government policy and legislative structures, military organisation, business and digital economic strength and levels of cyber social awareness. This information is distilled into an accessible format, using metrics to provide a snapshot by which government, business, and the public alike can garner an understanding of the cyber profile of regional actors

Evaluating the applicability of multi-agent software for implementing distributed industrial data management approaches

Distributed approaches to industrial control or information management problems are often tackled using Multi-agent methods. Multi-Agent systems – solutions resulting from taking a Multi-agent based approaches - often come with a certain amount of “overhead” such as communication systems, but can provide a helpful tool with the design and implementation. In this paper, a distributed data management problem is addressed with both a bespoke approach developed specifically for this problem and a more general Multi-agent approach. The two approaches are compared using architecture and software metrics. The software metric results show similar results, although overall the bespoke approach was more appropriate for the particular application examined. The architectural analysis indicates that the main reason for this difference is the communication and computation overhead associated with the agent-based system. It was not within the scope of this study to compare the two approaches under multiple application scenarios.BoeingThis is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/978-3-319-15159-5_1

Overcoming limited dataset availability when working with industrial organisations

Increasing data security and privacy requirements combined with the need for additional data management research leads to a conflict for industrial companies. In order to solve their industrial data management problems companies need to share some of their data, but their internal confidentiality rules sometimes hamper this sharing process. Existing techniques for sharing data without releasing company secrets often loose some of the problems/characteristics within the data. This paper therefore presents a qualitative process to overcome this problem of industrial data sharing while still enabling external researchers to develop relevant solutions to organizational problems. It is based on initial trials with two industrial case studies and showed some promising results.BoeingThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/INDIN.2015.728184

A framework for detecting unnecessary industrial data in ETL processes

Extract transform and load (ETL) is a critical process used by industrial organisations to shift data from one database to another, such as from an operational system to a data warehouse. With the increasing amount of data stored by industrial organisations, some ETL processes can take in excess of 12 hours to complete; this can leave decision makers stranded while they wait for the data needed to support their decisions. After designing the ETL processes, inevitably data requirements can change, and much of the data that goes through the ETL process may not ever be used or needed. This paper therefore proposes a framework for dynamically detecting and predicting unnecessary data and preventing it from slowing down ETL processes - either by removing it entirely or deprioritizing it. Other advantages of the framework include being able to prioritise data cleansing tasks and determining what data should be processed first and placed into fast access memory. We show existing example algorithms that can be used for each component of the framework, and present some initial testing results as part of our research to determine whether the framework can help to reduce ETL time.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/INDIN.2014.694555

An industrial data recommender system to solve the problem of data overload

This is the author accepted manuscript. The final version is available from 23rd European Conference on Information Systems (ECIS 2015) via https://balsa.man.poznan.pl/indico/event/44/contribution/203Getting the right data to the right decision-maker is a significant problem for many industrial companies. One of the main reasons is an overload of data. With the increasing amounts of industrial data this problem is becoming a bigger problem in the future. In order to address this challenge we propose the use of an Industrial Data Recommender System (IDRS). An IDRS recommends additional data to append to the data the decision-maker is currently working with, using techniques from the recommender systems domain like content-based and collaborative filtering. Using industrial cases we found that an IDRS is capable of suggesting useful information to the decision-maker. This additional information should help them to improve their decision-making.Boein

Evaluating the applicability of multi-agent software for implementing distributed industrial data management approaches

Distributed approaches to industrial control or information management problems are often tackled using Multi-agent methods. Multi-Agent systems- solutions resulting from taking a Multi-agent based approaches-often come with a certain amount of "overhead" such as communication systems, but can provide a helpful tool with the design and implementation. In this paper, a distributed data management problem is addressed with both a bespoke approach developed specifically for this problem and a more general Multi-agent approach. The two approaches are compared using architecture and software metrics. The software metric results show similar results, although overall the bespoke approach was more appropriate for the particular application examined. The architectural analysis indicates that the main reason for this difference is the communication and computation overhead associated with the agent-based system. It was not within the scope of this study to compare the two approaches under multiple application scenarios

Overcoming limited dataset availability when working with industrial organisations

Increasing data security and privacy requirements combined with the need for additional data management research leads to a conflict for industrial companies. In order to solve their industrial data management problems companies need to share some of their data, but their internal confidentiality rules sometimes hamper this sharing process. Existing techniques for sharing data without releasing company secrets often loose some of the problems/characteristics within the data. This paper therefore presents a qualitative process to overcome this problem of industrial data sharing while still enabling external researchers to develop relevant solutions to organizational problems. It is based on initial trials with two industrial case studies and showed some promising results