Early Accurate Results for Advanced Analytics on MapReduce

Approximate results based on samples often provide the only way in which advanced analytical applications on very massive data sets can satisfy their time and resource constraints. Unfortunately, methods and tools for the computation of accurate early results are currently not supported in MapReduce-oriented systems although these are intended for `big data'. Therefore, we proposed and implemented a non-parametric extension of Hadoop which allows the incremental computation of early results for arbitrary work-flows, along with reliable on-line estimates of the degree of accuracy achieved so far in the computation. These estimates are based on a technique called bootstrapping that has been widely employed in statistics and can be applied to arbitrary functions and data distributions. In this paper, we describe our Early Accurate Result Library (EARL) for Hadoop that was designed to minimize the changes required to the MapReduce framework. Various tests of EARL of Hadoop are presented to characterize the frequent situations where EARL can provide major speed-ups over the current version of Hadoop.Comment: VLDB201

Enhancing Decision-Making In SCM: Investigating The Status Quo And Obstacles Of Advanced Analytics In Austrian Companies

Over the past few years, the stability and predictability of logistics and supply chain networks have significantly decreased. This has led to higher risks and increased uncertainty in decision-making within supply chain management (SCM). Fortunately, the abundance of available data presents a tremendous opportunity to alleviate this uncertainty. However, realizing the full potential of advanced analytics, such as predictive and prescriptive analytics, is hindered by a lack of knowledge regarding their practical applications and performance benefits, as well as a deficiency in implementation expertise. This research paper examines the current state of advanced analytics applications and the primary challenges faced by Austrian companies in this domain. The findings reveal a distinct pattern: although the literature highlights numerous performance advantages, the practical utilization of advanced analytics remains at a rudimentary stage and is primarily confined to isolated departments. While demand management, procurement, and transport planning have shown some initial success in their implementation, other areas like production planning and, particularly, warehouse management lag. The primary challenges observed in practice include a limited understanding of the potential of advanced analytics, lack of transparency and data quality issues, difficulties in internal marketing, and inadequate organizational integration. These challenges, along with potential courses of action, serve as a starting point for other companies aiming to address similar issues. The significance of this work lies not only in its theoretical contribution to existing research on advanced analytics in SCM but also as one of the few studies that delve into the practical implementation and specific application domains of advanced analytics in Austria

READINESS OF LATVIA’S ORGANIZATIONS FOR ADVANCED ANALYTICS

The advanced analytics is one of the core tools to provide competitive advantage, sustainable development and foster productivity of the organization. Digital transformation and advanced analytics are two key trends in the emerging age of data, analytics, and automation. Digital transformation is the process of transforming how businesses operate when faced with digital disruption. Advanced analytics is the application of predictive and prescriptive models to analyse large, complex datasets in order to make critical business decisions. The focus of the paper is to assess the maturity level of advanced analytics in the organizations of Latvia by region, size and industry. Assessment was done by several domains like Organization, People, Data, Analytics, Technologies. The quantitative online survey was performed to assess the readiness of Latvia’s organizations for advanced analytics. The questionnaire was developed based on an academic literature review, reports and publications by researchers, analytical sector, industry experts and Author’s professionals experience in advanced analytics industry. The overall readiness level of Latvia’s organizations is 2.4 in 5 points scale. It differs by region, size of the organization and industry. Most of organizations do not have Analytics strategy, majority use spreadsheets based analytical tools, half of organizations use mostly only internal data, more than third part of organizations do not have any analytical resources. It leads to conclusion that majority of Latvia’s organizations are far from ability to improve productivity, be able to maximize the potential of the digital environment, to exploit data to make data-driven and automated decisions and are far from 21st century digital opportunities. Thus, puts under danger the sustainability of the organizations itself.

Institutional Research and Advanced Analytics

Tableau empowers everyone to see and understand their data. The Institutional Research & Advanced Analytics (IRAA) team will deliver an engaging one-hour demonstration that covers the ins and outs of Tableau Desktop and Tableau Server. We will also show you University of Kentucky data that is currently available to you, as well as where to find and how to access said data. The presentation slides are available by clicking the Download button on the right. The video and audio files of this workshop are listed as additional files below and are available for download

Advanced analytics as a tool to identify ways to achieve sustainable development

At this stage of information society is a rational mechanism for the achievement of sustainable development through the use of management information systems. Advanced Analytic System allows "deep" data mining, forecasting and optimization decision making. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3180

Measuring Greatness in the NBA

The “greatest player ever” debate stems from the controversy of how to measure a player’s effectiveness and contributions. Some analysts focus their arguments on a player’s statistics and advanced analytics. Another analyst may argue that awards play the largest role in a player’s worth to a team. Even though the tendency is to focus on one category of comparison, a player’s career is too complex to use only one category to rank players. In basketball, there are also so many exceptions in these points of comparison due to the team aspect of the sport. These factors all play a role in determining who is the greatest NBA player of all-time

THE ADVANCED ANALYTICS JUMPSTART: DEFINITION, PROCESS MODEL, BEST PRACTICES

Companies are encouraged by the big data trend to experiment with advanced analytics and many turn to specialist consultancies to help them get started where they lack the necessary competences. We investigate the program of one such consultancy, Advectas - in particular the advanced analytics Jumpstart. Using qualitative techniques including semi structured interviews and content analysis we investigate the nature and value of the Jumpstart concept through five cases in different companies. We provide a definition, a process model and a set of thirteen best practices derived from these experiences, and discuss the distinctive qualities of this approach

Advanced analytics for transformer asset management

Power transformers are one of the most crucial components of any power system network. A new asset management software called APM Edge, based on the reliability centred maintenance (RCM) methodology for the fleet-wide assessment of power transformers that utilises the principle of fault tree analysis is now available. This analytical software is an expert system that incorporates a probabilistic model which always assigns a risk factor to any given transformer – both for longterm reliability and short-term functionality. This paper presents a case study on the utilisation of this expert system and analytical software on a 25 MVA transformer which helped in: • DGA data quality identification • Predicting future dissolved gas trends • Predicting when the DGA abnormal levels would be reached • Time available before the shutdown • Determining what investigations are required