A method to measure the accounting abnormal returns of largescale information technology investments: the case of enterprise systems

There is a considerable body of literature about the business value of information technology. Although there is empirical evidence about the positive impact of large-scale information technology on firm performance, the number and variety of quantitative methods used to measure this impact is considerable. Besides this diversity in the methods, almost none of them have both strong theoretical basis and strong econometric robustness. A method to measure accounting-based abnormal returns of large-scale information technology is proposed. Unlike existing accounting and market measures, this measure considers industry tendencies over time and the magnitude of the measure can be used as a proxy of the business value of the IT initiative. The method is implemented using a sample of enterprise systems implementations in public companies. This is a unique methodology based on recent accounting research, which propose an econometric model to capture annual abnormal returns of large-scale information technology initiatives. The method is validated with theoretical arguments and empirical results. Empirical results suggest that the measurement of the IT payoff is reliable, valid and robust

A correlation-aware data placement strategy for key-value stores

Key-value stores hold the unprecedented bulk of the data produced by applications such as social networks. Their scalability and availability requirements often outweigh sacri cing richer data and pro- cessing models, and even elementary data consistency. Moreover, existing key-value stores have only random or order based placement strategies. In this paper we exploit arbitrary data relations easily expressed by the application to foster data locality and improve the performance of com- plex queries common in social network read-intensive workloads. We present a novel data placement strategy, supporting dynamic tags, based on multidimensional locality-preserving mappings. We compare our data placement strategy with the ones used in existing key-value stores under the workload of a typical social network appli- cation and show that the proposed correlation-aware data placement strategy o ers a major improvement on the system's overall response time and network requirements

On the expressiveness and trade-offs of large scale tuple stores

Proceedings of On the Move to Meaningful Internet Systems (OTM)Massive-scale distributed computing is a challenge at our doorstep. The current exponential growth of data calls for massive-scale capabilities of storage and processing. This is being acknowledged by several major Internet players embracing the cloud computing model and offering first generation distributed tuple stores. Having all started from similar requirements, these systems ended up providing a similar service: A simple tuple store interface, that allows applications to insert, query, and remove individual elements. Further- more, while availability is commonly assumed to be sustained by the massive scale itself, data consistency and freshness is usually severely hindered. By doing so, these services focus on a specific narrow trade-off between consistency, availability, performance, scale, and migration cost, that is much less attractive to common business needs. In this paper we introduce DataDroplets, a novel tuple store that shifts the current trade-off towards the needs of common business users, pro- viding additional consistency guarantees and higher level data process- ing primitives smoothing the migration path for existing applications. We present a detailed comparison between DataDroplets and existing systems regarding their data model, architecture and trade-offs. Prelim- inary results of the system's performance under a realistic workload are also presented

Research in particle and gamma-ray astrophysics

Research activities in cosmic rays, gamma rays, and astrophysical plasmas are covered. Each activity is described, followed by a bibliography. The research program is directed toward the investigation of the astrophysical aspects of cosmic rays and gamma rays and of the radiation and electromagnetic field environment of the earth and other planets. These investigations were performed by means of energetic particle and photon detector systems flown on spacecraft and balloons

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Co-Benefits of Largescale Organic farming On huMan health (BLOOM)::Protocol for a cluster-randomised controlled evaluation of the Andhra Pradesh Community-managed Natural Farming programme in India

The BLOOM study (co-Benefits of Largescale Organic farming On huMan health) aims to determine if a government-implemented agroecology programme reduces pesticide exposure and improves dietary diversity in agricultural households. To achieve this aim, a community-based, cluster-randomised controlled evaluation of the Andhra Pradesh Community-managed Natural Farming (APCNF) programme will be conducted in 80 clusters (40 intervention and 40 control) across four districts of Andhra Pradesh state in south India. Approximately 34 households per cluster will be randomly selected for screening and enrolment into the evaluation at baseline. The two primary outcomes, measured 12 months post-baseline assessment, are urinary pesticide metabolites in a 15% random subsample of participants and dietary diversity in all participants. Both primary outcomes will be measured in (1) adult men ≥18 years old, (2) adult women ≥18 years old, and (3) children <38 months old at enrolment. Secondary outcomes measured in the same households include crop yields, household income, adult anthropometry, anaemia, glycaemia, kidney function, musculoskeletal pain, clinical symptoms, depressive symptoms, women’s empowerment, and child growth and development. Analysis will be on an intention-to-treat basis with an a priori secondary analysis to estimate the per-protocol effect of APCNF on the outcomes. The BLOOM study will provide robust evidence of the impact of a large-scale, transformational government-implemented agroecology programme on pesticide exposure and dietary diversity in agricultural households. It will also provide the first evidence of the nutritional, developmental, and health co-benefits of adopting agroecology, inclusive of malnourishment as well as common chronic diseases

Towards cloud based big data analytics for smart future cities

© 2015, Khan et al.; licensee Springer. A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends

Spatial co‐localisation of extreme weather events: a clear and present danger

Extreme weather events have become a dominant feature of the narrative surrounding changes in global climate with large impacts on ecosystem stability, functioning and resilience; however, understanding of their risk of co‐occurrence at the regional scale is lacking. Based on the UK Met Office’s long‐term temperature and rainfall records, we present the first evidence demonstrating significant increases in the magnitude, direction of change and spatial co‐localisation of extreme weather events since 1961. Combining this new understanding with land‐use data sets allowed us to assess the likely consequences on future agricultural production and conservation priority areas. All land‐uses are impacted by the increasing risk of at least one extreme event and conservation areas were identified as the hotspots of risk for the co‐occurrence of multiple event types. Our findings provide a basis to regionally guide land‐use optimisation, land management practices and regulatory actions preserving ecosystem services against multiple climate threats