Barriers to the implementation of cloud computing for sustainable construction in a developing economy

PurposeThe purpose of this paper is to investigate the applicability of cloud computing (CC) and the challenges that contribute to more successful projects with a major sustainable construction development.Design/methodology/approachThe previous studies provided information on CC implementation barriers, which were then evaluated by 104 construction stakeholders through a questionnaire survey. As a result, the exploratory factor analysis (EFA) approach was used to investigate these barriers. Furthermore, a partial least square structural equation model was used to build a model of these barriers (PLS-SEM).FindingsThe EFA results revealed that the above-noted factors are in a close relation with three key components, i.e. social, economic and communication. In addition, the proposed model results found the social barrier a key challenge to the implementation of CC.Research limitations/implicationsThe results from this study can help decision-makers to improve the approaches regarding data fragmentation that has great effects on the execution of all construction projects. The focus of the paper is to enhance the data fragmentation processes. In addition, the results would be useful to strengthen the sustainability of existing construction projects by enhancing the implementation of CC.Originality/valueThe novelty of this research work will provide a solid foundation for critically assessing and appreciating the different barriers affecting the adoption of CC.

Improving the Performance of Deduplication-Based Backup Systems via Container Utilization Based Hot Fingerprint Entry Distilling

Data deduplication techniques construct an index consisting of fingerprint entries to identify and eliminate duplicated copies of repeating data. The bottleneck of disk-based index lookup and data fragmentation caused by eliminating duplicated chunks are two challenging issues in data deduplication. Deduplication-based backup systems generally employ containers storing contiguous chunks together with their fingerprints to preserve data locality for alleviating the two issues, which is still inadequate. To address these two issues, we propose a container utilization based hot fingerprint entry distilling strategy to improve the performance of deduplication-based backup systems. We divide the index into three parts: hot fingerprint entries, fragmented fingerprint entries, and useless fingerprint entries. A container with utilization smaller than a given threshold is called a sparse container . Fingerprint entries that point to non-sparse containers are hot fingerprint entries. For the remaining fingerprint entries, if a fingerprint entry matches any fingerprint of forthcoming backup chunks, it is classified as a fragmented fingerprint entry. Otherwise, it is classified as a useless fingerprint entry. We observe that hot fingerprint entries account for a small part of the index, whereas the remaining fingerprint entries account for the majority of the index. This intriguing observation inspires us to develop a hot fingerprint entry distilling approach named HID . HID segregates useless fingerprint entries from the index to improve memory utilization and bypass disk accesses. In addition, HID separates fragmented fingerprint entries to make a deduplication-based backup system directly rewrite fragmented chunks, thereby alleviating adverse fragmentation. Moreover, HID introduces a feature to treat fragmented chunks as unique chunks. This feature compensates for the shortcoming that a Bloom filter cannot directly identify certain duplicated chunks (i.e., the fragmented chunks). To take full advantage of the preceding feature, we propose an evolved HID strategy called EHID . EHID incorporates a Bloom filter, to which only hot fingerprints are mapped. In doing so, EHID exhibits two salient features: (i) EHID avoids disk accesses to identify unique chunks and the fragmented chunks; (ii) EHID slashes the false positive rate of the integrated Bloom filter. These salient features push EHID into the high-efficiency mode. Our experimental results show our approach reduces the average memory overhead of the index by 34.11% and 25.13% when using the Linux dataset and the FSL dataset, respectively. Furthermore, compared with the state-of-the-art method HAR, EHID boosts the average backup throughput by up to a factor of 2.25 with the Linux dataset, and EHID reduces the average disk I/O traffic by up to 66.21% when it comes to the FSL dataset. EHID also marginally improves the system's restore performance.

The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying “the hot spots” of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world.

Cdep: Qos-Aware Crowd-Deduplication with Efficient Data Placement in Big Data Analytics

In current world, on account of tremendous enthusiasm for the big data extra space there is high odds of data duplication. Consequently, repetition makes issue by growing extra room in this manner stockpiling cost. Constant assessments have shown that moderate to high data excess obviously exists in fundamental stockpiling structures in the big data specialist. Our test thinks about uncover those data plenitude shows and a lot further degree of power on the I/O way than that on hovers because of for the most part high common access an area related with little I/O deals to dull data. Furthermore, direct applying data deduplication to fundamental stockpiling structures in the big data laborer will likely explanation space struggle in memory and data fragmentation on circles. We propose a genuine exhibition arranged I/O deduplication with cryptography, called CDEP (crowd deduplication with effective data placement), and rather than a limit situated I/O deduplication. This technique achieves data sections as the deduplication system develops. It is imperative to separate the data pieces in the deduplication structure and to fathom its features. Our test assessment utilizing authentic follows shows that contrasted and the progression based deduplication calculations, the copy end proportion and the understanding presentation (dormancy) can be both improved at the same time.

The “Sticky Notes” Method: Adapting Interpretive Description Methodology for Team-Based Qualitative Analysis in Community-Based Participatory Research

Community-based participatory research (CBPR) has a long history within HIV research, yet little work has focused on facilitating team-based data analysis within CBPR. Our team adapted Thorne’s interpretive description (ID) for CBPR analysis, using a color-coded “sticky notes” system to conduct data fragmentation and synthesis. Sticky notes were used to record, visualize, and communicate emerging insights over the course of 11 in-person participatory sessions. Data fragmentation strategies were employed in an iterative four-step process that was reached by consensus. During synthesis, the team created and recreated mind maps of the 969 sticky notes, from which we developed categories and themes through discussion. Flexibility, trust, and discussion were key components that facilitated the evolution of the final process. An interactive, team-based approach was central to data co-creation and capacity building, whereas the “sticky notes” system provided a framework for identifying and sorting data.

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

A spatiotemporal object-oriented data model for landslides (LOOM)

LOOM (landslide object-oriented model) is here presented as a data structure for landslide inventories based on the object-oriented paradigm. It aims at the effective storage, in a single dataset, of the complex spatial and temporal relations between landslides recorded and mapped in an area and at their manipulation. Spatial relations are handled through a hierarchical classification based on topological rules and two levels of aggregation are defined: (i) landslide complexes, grouping spatially connected landslides of the same type, and (ii) landslide systems, merging landslides of any type sharing a spatial connection. For the aggregation procedure, a minimal functional interaction between landslide objects has been defined as a spatial overlap between objects. Temporal characterization of landslides is achieved by assigning to each object an exact date or a time range for its occurrence, integrating both the time frame and the event-based approaches. The sum of spatial integrity and temporal characterization ensures the storage of vertical relations between landslides, so that the superimposition of events can be easily retrieved querying the temporal dataset. The here proposed methodology for landslides inventorying has been tested on selected case studies in the Cilento UNESCO Global Geopark (Italy). We demonstrate that the proposed LOOM model avoids data fragmentation or redundancy and topological inconsistency between the digital data and the real-world features. This application revealed to be powerful for the reconstruction of the gravity-induced deformation history of hillslopes, thus for the prediction of their evolution.

Security Enhanced Model for Cloud Data Based on Dynamic Data Fragmentation and Replication (DDFR)

Nowadays cloud computing is utilized in several IT capabilities like smart industry with IoTs, Mobile computing, etc., It is happen through outsourcing data to a third-party administrative control which is great application of cloud. But this leads to data leakage through attacks. A high level of data security is required on the data stored in cloud nodes. This paper enhances the security and data availability by dynamic fragmentation and replication process. The fragmentation is performed in runtime to create the fragments according to the available virtual machine. The replication aims to enhance the better load balance with less number of replicas. Bee colony algorithm is used for finding the best node in replication. The AES encryption approach is used for encrypting the fragments. This approach does not provides the original data if any attacks happened successfully.