Data Management

Unique in its integration of theory and application, this comprehensive book explains survey design, implementation, data analysis, and continuing data management, including how to effectively incorporate the latest technology (e.g., SurveyMonkey and Qualtrics). Data management and analysis are demonstrated and explained through statistical software including SPSS, SAS, and Stata. In addition to helping students develop a complete understanding of survey research from start to finish, the authors also address the challenges and issues of specific disciplines.

This essay aims to address a general conception of what privacy is, as well as an analysis of the challenges and dangers of living without privacy and the importance of data protection against the breakdown of traditional paradigms by new technologies disruptive, concluding that cyber literacy and cyber hygiene are fundamental pillars to combat threats, disclosures, risks and vulnerabilities that could become security incidents that threaten arbitrary interference with private life.

The tremendous advances in wireless networks, mobile computing, and sensor networks, along with the rapid growth of small, portable and powerful computing devices, offers more and more opportunities for pervasive computing and communications. This topic deals with cutting-edge research in various aspects related to the theory and practice of mobile computing or wireless and mobile networking. These aspects include architectures, algorithms, networks, protocols, modeling and performance issues, data management, ...

Aalto New Campus Complex (ANCC) is a recently inaugurated educational facility at Aalto University, located in Otaniemi (Espoo), Finland. Within over 40,000 m2, it comprises two faculties, a shopping center, recreational areas, and a metro station. ANCC is also a large-scale application of Ground Source Heat Pump (GSHP)–Borehole Thermal Energy Storage (BTES) in Finland, comprising an irregular BTES field of 74 boreholes with an overall length of roughly 23 km and 4 million m3 of energy storage. Therefore, accurate monitoring of the GSHP–BTES energy system is crucial for sustainable and efficient long-term operation. Due to the fundamental issues affecting the accuracy of all thermal energy meters, a novel methodology adjusting for consistency of the measured data (in order to accomplish daily energy balance on both sides of the GSHP) is developed. The proposed methodology is used also in conjunction with reconstruction of missing relevant data before April/May 2020 by applying linea...

World crude oil production tends to continue to decline if not balanced with discovery of oil and gas fields. Nowadays oil and gas exploration activities are being be perceived sluggish. Statistical data issued by SKK Migas where there were 318 Work Areas in 2014, down to 212 Areas in April 2019 and as many as 52% were still in exploration stage. Reserves found are not comparable to those exploited, Indonesia's Reserve Replacement Ratio (RRR) which is only 50%. The exploration phase requires high costs and a long time, the costs of exploration from exploration studies to drilling exploration is US$ 1-100 million and exploration periods is 6-10 years. Purpose of this method is to optimize costs and time in exploration activities, through big data exploration activities with the petroleum system as the fundamental. The method is to explore the database based on parameters of petroleum system which will be classified (ranking) of each element of petroleum system which is extracted based on the amount of data publications, geological & geophysical survey data and historical exploration of basin are used as reference to give confident in making decisions to enter drilling exploration stage. This method can be applied to mature basins with assumtion fairly complete database. With big data exploration we can optimize exploration costs, especially the costs of drilling exploration and big data exploration activities require a shorter time. The advantages of using this scheme can provide an overview of basin evaluations based on elements of petroleum system so that we have an overview and plan of actions to be carried out in accordance with results from classification. In addition, this scheme will optimize use of existing exploration data and recommend activities for updating the data.

While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of conventional software and hardware. Hadoop framework distributes large datasets over multiple commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop framework and propose methods for enhancing I/O performance. A proven approach is to cache data to maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node combining design which extends the traditional combiner to a node level. The in-node combiner reduces the total number of intermediate results and curtail network traffic between mappers and reducers.

PurposeThe purpose of this study is to determine the underlying dimensions of supply chain management (SCM) practices and to empirically test a framework identifying the relationships among SCM practices, operational performance and SCM‐related organizational performance with special emphasis on small and medium size enterprises (SMEs) in Turkey.Design/methodology/approachData for the study were collected from a sample of 203 manufacturing SMEs operating in the manufacture of fabricated metal products and general purpose machinery (NACE codes 28 and 29) within the city of Istanbul in Turkey. The research framework was tested using partial least squares method, which is a variance‐based structural equation modeling approach.FindingsBased on exploratory factor analysis (EFA), SCM practices were grouped in two factors: outsourcing and multi‐suppliers (OMS), and strategic collaboration and lean practices (SCLP). The results indicate that both factors of SCLP and OMS have direct positive...

This paper describes key aspects of the structural and functional transformation of the Russian state statistics system as a core element of the future National Data Management System. Issues such as establishing a dialogue between the statistics service and users, integrating data from various sources, and intelligent data processing in the context of the digitalization of the economy are considered. New approaches and mechanisms should integrate and advance all of the previously achieved best results in methodology, observation areas, metrics, and other domains. Improvement areas include providing higher-quality information for policy shaping, businesses, individuals, and external partners. National statistics is expected to present an interconnected, objectively measurable model of socioeconomic processes and phenomena based on relevant theoretical concepts. In addition to using various sources of data, a necessary feature of the new system will be its reliance on a consistent conceptual framework and approaches to interpreting data, which will make it possible to integrate various data sources in the first place.

Users of intelligent statistics are becoming not only active participants in primary data collection, accumulation and application processes, but are turning into “smart” consumers who develop statistical thinking and are able to derive the greatest possible benefits from the use of statistical data. Such skills should become an inherent (and possibly mandatory) component of any.

These Tourism systems can be considered as an important motivation for tourists to visit the underlined places. These system employs the technologies, such as Internet of Tings(IoT), to manage the provided program and tourists by providing useful information. In this work, we state a survey of previous work on tourism system combined with IoT in terms of managing and analyzing of big data using data mining techniques. This is to extract important information from the huge data generated by the use of Internet of things in smart tourism system. By improving an algorithm for data classification algorithm, the proposed solution is formed to produce a classifier that can deals with different types of data with accepted complexity instead of using different classifiers for numeric data types. Moreover, it introduces an enhanced evaluation method, in which most of classifier can be included with high efficiency. The android based mobile application and web site can be produced to support the using of the proposed system.