Proper Data Management Research Articles

A Case Study on Importance of Tourism Policy in Promotion of Tourism with Reference to Maharashtra Tourism Policy

Tourism is fast growing industry with creating a billion job opportunities every year. Government is making the tourism policies for the development of the destination, their upkeep and maintenance. Policies implemented well help the tourism industry to achieve the better result. Tourism creates direct and indirect job opportunities and opens the various segments for revenue generation. Foreign exchange is important for any country to make the economy stronger; tourism is one of the service industries which support in economic development. Proper evaluation of the policy after certain period shared with the service provider and proper data management system for the same can help all the tourism support industry to improve on the services to achieve the great tourist satisfaction.

The role of big data, cloud computing and IoT to make cities smarter

Smart city enhanced the quality of life for the citizen through utilising information technology, networking and proper data management. It could involve the proper management of natural resources, infrastructure, power, transportation, healthcare, education, public safety and government, etc. If a city becomes smarter, the living standard improves. To make a city smarter, multiple technologies need to be strengthened in the area of various services. It reduces costs and resource consumption as well as more effective use of them. To enhance smart city services, big data, cloud computing and internet of things (IoT) plays a huge potential role. As digitisation was growing, huge data are obtained in every second. Effective data collection, storage, analysis and utilisation are a big key factor for successful smart cities. This paper, reviews the role of big data, cloud computing and IoT support smart cities. In addition, it identifies different perspectives, challenges and use of these technologies.

Resource Management through Machine Learning

In the modern era of diminishing returns on fixed exploration budgets, challenging targets, and ever-increasing numbers of multi-parameter datasets, proper management and integration of available data is a crucial component of any resource exploration program. Machine learning algorithms have successfully been used for years by the technology sector to accomplish just this task on their databases, and recent developments aim at appropriating these successes to the field of natural resource exploration. Numerous algorithms have been attempted for resource prospectivity mapping in the past, and in this paper we apply a modified support-vector machine algorithm to a test dataset from the QUEST region in central British Columbia, Canada, to target undiscovered Cu-Au porphyry districts. The modified algorithm is designed to properly handle the highly variable uncertainty associated with both the training data (ie: geophysics, geochemistry, geological mapping) as well as the training labels (known Cu-Au porphyry targets in the region). Support vector machines are introduced, the challenges of working with geoscientific datasets are discussed, and finally results from applying the modified algorithm to the QUEST dataset are presented.

The Georeferenced Digital Database for Sustainable Land Use Management in Namibia

The article presents the empirical findings on the georeferenced digital database for sustainable land use management of the Hardap region, Namibia. The research signifies the continuous value of geographical data management for land use planning and management in Namibia. The research applied a combination of approaches that include literature review, Geographical Information System (GIS), delineation of the imagery, Global Positioning Systems (GPS) and cartographic mapping. Both positive and negative impacts of the georeferenced digital database for sustainable land use management were explored. The research findings, among others, include the concepts, explanations, procedures and the value of the georeferenced digital database towards sustainable land use management. The research further provides important results such as existing organisational structures for spatial data management, elaborated procedures and steps for creating a georeferenced digital database for proper management of the GIS data in Namibia. Key recommendations include proper managed organisational structures for spatial data management, implementation of the elaborated procedures and steps for creating a georeferenced digital database for sustainable land use planning in Namibia.

MPD3: a useful medicinal plants database for drug designing

Medicinal plants are the main natural pools for the discovery and development of new drugs. In the modern era of computer-aided drug designing (CADD), there is need of prompt efforts to design and construct useful database management system that allows proper data storage, retrieval and management with user-friendly interface. An inclusive database having information about classification, activity and ready-to-dock library of medicinal plant’s phytochemicals is therefore required to assist the researchers in the field of CADD. The present work was designed to merge activities of phytochemicals from medicinal plants, their targets and literature references into a single comprehensive database named as Medicinal Plants Database for Drug Designing (MPD3). The newly designed online and downloadable MPD3 contains information about more than 5000 phytochemicals from around 1000 medicinal plants with 80 different activities, more than 900 literature references and 200 plus targets. The designed database is deemed to be very useful for the researchers who are engaged in medicinal plants research, CADD and drug discovery/development with ease of operation and increased efficiency. The designed MPD3 is a comprehensive database which provides most of the information related to the medicinal plants at a single platform. MPD3 is freely available at: http://bioinform.info.

FoodCASE: A system to manage food composition, consumption and TDS data

Food and nutrition scientists, nowadays, need to manage an increasing amount of data regarding food composition, food consumption and Total Diet Studies (TDS). The corresponding datasets can contain information about several thousand different foods, in different versions from different studies. FoodCASE is a system that has been developed to manage these different datasets. It also support flexible means of linking between datasets and generally provide support for the different processes involved in the acquisition, management and processing of data. In this paper, the most important concepts to implement existing guidelines and standards for proper food data management are presented, as well as different use cases of data import and proofs of concepts demonstrating the ability to manage data in FoodCASE.

High-resolution 3D surface modeling of a fossil oyster reef

The world’s largest fossil oyster reef, formed by the giant oyster Crassostrea gryphoides and located in Stetten (north of Vienna, Austria), is studied in this article. Digital documentation of the unique geological site is provided by terrestrial laser scanning (TLS) at the millimeter scale. Obtaining meaningful results is not merely a matter of data acquisition with a suitable device; it requires proper planning, data management, and postprocessing. Terrestrial laser scanning technology has a high potential for providing precise 3D mapping that serves as the basis for automatic object detection in different scenarios; however, it faces challenges in the presence of large amounts of data and the irregular geometry of an oyster reef. We provide a detailed description of the techniques and strategy used for data collection and processing. The use of laser scanning provided the ability to measure surface points of 46,840 (estimated) shells. They are up to 60-cm-long oyster specimens, and their surfaces are modeled with a high accuracy of 1 mm. In addition, we propose an automatic analysis method for identifying and enumerating convex parts of shells. Object surfaces were detected with a completeness of 69% and a correctness of over 75% by means of a fully automated workflow. Accuracy of 98% was achieved in detecting the number of objects. In addition to laser scanning measurements, more than 300 photographs were captured, and an orthophoto mosaic was generated with a ground sampling distance (GSD) of 0.5 mm. This high-resolution 3D information and the photographic texture serve as the basis for ongoing and future geological and paleontological analyses. Moreover, they provide unprecedented documentation for conservation issues at a unique natural heritage site.

Model to Quantify Integrity at Requirement Phase

Objectives: The software security and its proper measurement involve various tools and techniques. To make software secure its foundation stone, the requirement, should also be secure and therefore the integrity control for requirement at the same phase became the objective for delivering secure requirement and so forth the software. Method/Analysis: The past research study entails about less secure software deliver by industries and among the major cause of security lacking, one cause is integrity to requirements. Requirement integrity can be observed as trustworthy, complete and veracity requirement for producing secure software. Only conceptual notion of integrity is not capable to make any component secure but it must be depicted in understandable and quantifiable idiom for assessing the better security solution to requirement and therefore to software. Findings: MQIR (Model to quantify integrity) for requirement at requirement phase of software development process is a new assessment technique that tries to satisfy all major constraints regarding secure requirements and so forth for software. To implement this new technique a relevant set of values in terms of input is captured for proper data management from various live projects under going in some software companies of repute. An appropriate quantification of requirement data is also furnished for sufficient checking of security level and its efficiency. The technique has also been validated through capturing online shopping data so that its implications may also be assessed accurately. Novelty/Improvement: As far as the application of this technique (MQIR) is concern, the article emphasis on complete usability of online functioning at its highest level of integrity that maintains security with the boundary conditions of e-commerce.

The Serial Commutator FFT

This brief presents a new type of fast Fourier transform (FFT) hardware architectures called serial commutator (SC) FFT. The SC FFT is characterized by the use of circuits for bit-dimension permutation of serial data. The proposed architectures are based on the observation that, in the radix-2 FFT algorithm, only half of the samples at each stage must be rotated. This fact, together with a proper data management, makes it possible to allocate rotations only every other clock cycle. This allows for simplifying the rotator, halving the complexity with respect to conventional serial FFT architectures. Likewise, the proposed approach halves the number of adders in the butterflies with respect to previous architectures. As a result, the proposed architectures use the minimum number of adders, rotators, and memory that are necessary for a pipelined FFT of serial data, with 100% utilization ratio.

Comprehensive 3D Measurements of Tram Tracks in the Tunnel Using the Combination of Laser Scanning Technology and Traditional TPS/GPS Surveying

Urban Transport systems in big cities face many challenges because of continuous growth of the urban population, amount of cars and general overload of the public transportation. Challenges which the urban transport is facing have consequences for households, businesses and the whole urban community. Urban transportation takes a special part in linking economic growth and collective development as well as extensive negative impact on citizens’ health and surrounding environment. Many financial institutions and governments support cities and countries in their endeavor to build and improve transport infrastructure and systems to solve above issues.Within the other modern cities also Kraków with its almost 800 thousand people population is being challenged also in this regards. During the recent decades there has been done a major change in its urban transportation network. Trams rails network with a modern tram machines was defined as the one of the most important for the system. In Kraków the project is called “Krakow's Fast Tram”. Along with this extraordinary development there needs to be adopted and efficiently used a way to verify and maintain transportation infrastructure. Among the others the laser scanning with its incredible functionality and speed is becoming an answer for this demand. In the lane with data capturing the proper data management and access is coming. The authors used and underlined a big benefit of the combination of available metrology techniques including accurate total station measurements, laser scanning and GPS positioning. The project was started by measuring the Fast Tram Tunnel in Kraków with techniques mentioned above. The next step was to post process the data and assure proper quality so it can be used in all disciplines. To illustrate this innovative combination of techniques and possible use for various disciplines there has been prepared verification of transition curve, circulation curve, track gauge, cant and other parameters which are standard deliverables for tram track geometry studies. On the other hand the scan data management portal has been created with scan data available through the web site regardless the location.Finally with regards to the rapid development of the city infrastructure and community it is extremely important to use all currently available measuring techniques in an efficient and modern way which has to be fast and flexible enough to cope current demands. In this particular study laser scanning together with other surveying and analytics techniques helps to verify and maintenance tram infrastructure in urban transportation today and very likely will be the best method as well in the future.

Data management in maintenance outsourcing

Most businesses view maintenance as tasks carried out by technicians and the data collected is mostly cost related. There is a growing trend towards outsourcing of maintenance and the data collection issues are not addressed properly in most maintenance service contracts. Effective maintenance management requires proper data management - data collection and analysis for decision-making. This requires a proper framework and when maintenance is outsourced it raises several issues and challenges. The paper develops a framework for data management when maintenance is outsourced and looks at a real case study that highlights the need for proper data management.

Variable Selection Methods for Right-Censored Time-to-Event Data with High-Dimensional Covariates

Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations.

An integrated algorithm for local sequence alignment

Local sequence alignment (LSA) is an essential part of DNA sequencing. LSA helps to identify the facts in biological identity, criminal investigations, disease identification, drug design and research. Large volume of biological data makes difficulties to the performance of efficient analysis and proper management of data in small space has become a serious issue. We have subdivided the data sets into various segments to reduce the data sets as well as for efficient memory use. The integration of dynamic programming (DP) and Chapman–Kolmogorov equations (CKE) makes the analysis faster. The subdivision process is named data reducing process (DRP). DRP is imposed before DP and CKE. This approach needs less space compared with other methods and the time requirement is also improved.

Intelligent Health Monitoring Based on Pervasive Technologies and Cloud Computing

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising.

Hungarian Hypertension Registry

Today, hypertension is considered endemic throughout the world. The number of individuals with high blood pressure and the increasing risk, morbidity and mortality caused by hypertension despite modern therapy do not decrease sufficiently. Hypertension has become a public health issue. Prevention and effective care require integrated datasets about many features, clinical presentation and therapy of patients with hypertension. The lack of this database in Hungary prompted the development of the registry which could help to provide population-based data for analysis. Data collection and processing was initiated by the Hungarian Society of Hypertension in 2002. Data recording into the Hungarian Hypertension Registry was performed four times (2002, 2005, 2007, 2011) and the registry currently contains data obtained from 108,473 patients. Analysis of these data indicates that 80% of the patients belong to the high or very high cardiovascular risk group. The registry provides data on cardiovascular risk of the hypertensive populations and the effectiveness of antihypertensive therapy in Hungary. Based on international experience and preliminary analysis of data from the Hungarian Hypertension Registry, establishment of hypertension registry may support the effectiveness of public health programs. A further step would be needed for proper data management control and the application of professional principles of evidence-based guidelines in the everyday practice.

Multidisciplinary Decision-Making Approach to High-Dimensional Event History Analysis through Variable Reduction Methods

As an analytical approach, decision-making is the process of finding the best option from all feasible alternatives. The application of decision- making process in economics, management, psychology, mathematics, statistics and engineering is obvious and this process is an important part of all science-based professions. Proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision-making. This paper presents a class of practical methods to analyze high-dimensional event history data to reduce redundant information and facilitate practical interpretation through variable inefficiency recognition. In addition, numerical experiments and simulations are developed to investigate the performance and validation of the proposed methods.

Morphology of time data management - systematic design of time data management processes as fundamental challenge in industrial engineering

During the last 10 to 15 years, a renaissance of industrial engineering can be observed in manufacturing industry, as well as in scientific research. As a consequence, time and motion studies (TMS) become more important again. During the downturn of methodical approaches in industrial engineering in the 1980s, TMS-related competencies were lost in industry. Many companies are still missing the know-how for establishing a proper time data management (TDM). This paper presents a morphology of time data management (MoTDM), which was developed in order to create a comprehensive view on the processes of TDM as well as to detect relevant areas of improvement. The MoTDM can be used to optimise TDM processes within a company and also acts as scientific fundament for research in the field of industrial engineering.

Basic Principles for Conducting Human Research in Orthopaedic Medicine

Researchers and clinicians operate in an increasingly complex clinical and regulatory environment in which understanding the principles governing human research is essential. However, most orthopaedic surgeons have not received in-depth training in regulatory requirements and scientific research methods. Ensuring that research is conducted in accordance with state and federal laws and ethical principles is essential to guard compromising patient information and avoid severe penalties for noncompliance. The researcher must understand the regulations for compliance and proper data management, including the requirements of the Health Insurance Portability and Accountability Act, proper application of informed consent, use of the Institutional Review Board, and data protection guidelines. Tools such as a regulatory binder can assist investigators in complying with requirements, maintaining regulatory standards, and ensuring a robust study design and conduct.

Basic Principles for Conducting Human Research in Orthopaedic Medicine

Researchers and clinicians operate in an increasingly complex clinical and regulatory environment in which understanding the principles governing human research is essential. However, most orthopaedic surgeons have not received in-depth training in regulatory requirements and scientific research methods. Ensuring that research is conducted in accordance with state and federal laws and ethical principles is essential to guard compromising patient information and avoid severe penalties for noncompliance. The researcher must understand the regulations for compliance and proper data management, including the requirements of the Health Insurance Portability and Accountability Act, proper application of informed consent, use of the Institutional Review Board, and data protection guidelines. Tools such as a regulatory binder can assist investigators in complying with requirements, maintaining regulatory standards, and ensuring a robust study design and conduct.

QoS- Continuous Live Media Streaming in Mobile Environment using VBR and Edge Network

Live media services in mobile environments are fetching more essential with the explosion of technologies. Live Media streaming, in particular, is a promising technology for providing services such as live news clips, live sports. To avoid service interruption when the users keep moving, proper data management strategies must be engaged. In an earlier paper, the ideas and benefits of two-level cooperative media streaming with headlight pre fetching and dynamic chaining were demonstrated and they using static bit rate delivery. In this paper, we propose the QoS-based dynamic adaptation techniques for the flexible employment and smooth integration of headlight pre-fetching and dynamic chaining to continuously provide quality streaming services to mobile users. And also it provides Variable bit rate delivery. We propose a new mechanism Upload Cache in Edge Networks brings benefit for both end users and service providers. For end users, it shortens the duration while user must stay online for uploading their generated content. Also for service providers, it reduces peak traffic volume between edge networks and data centers by slightly shifting the upload timing without incurring much extra latency overhead added. Our analysis with replaying the captured traffic shows that this mechanism reduces upload tether time of 24% end users by more than half and flattens the traffic peak for the access service provider by 37%. To avoid disconnection and/or service breakdown when the users keep moving, proper data management strategies must be taken by all parties. We propose a two level framework and a set of new techniques for cooperative media streaming in mobile environments.