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Purpose. To offer a range of practical suggestions for library staff and others who intend to collect and analyze data that describe their networked services. Method/approach. Based on the author's experience in completing a number of funded research projects on this topic, working directly with libraries in collecting networked statistics and implementing the findings from those efforts, and observing best practices at those libraries. Findings. Factors dealing with understanding the evaluation context, planning, training, organization, determining appropriate data collection methods, managing data, and knowing the target audience for reporting are all‐important. Details a number of specific factors and strategies that can be used to increase the success of collecting networked statistics. Practical implications. Thinking about the preparation needed to collect networked statistics and how the process can be handled as efficiently as possible before the effort begins can help ensure that the library staff uses its time as wisely as possible in the data collection process, collects high quality and useful data, integrates these data into other library data, can draw upon the data for future purposes through a management information system, and uses the data to improve overall library decision making, planning, and meeting community information needs.

The online platform is one of the essential components of the platform economy that is constructed by a large scale of the personal data resource. However, accurate empirical test of the competition structure of the data-driven online platform is still less. This research is trying to reveal market allocation structure of the personal data resource of China's car-hailing platforms competition by the empirical data analysis.

This research is applying the social network analysis by R packages, which include k-core decomposition and multilevel community detection from the data connectedness via the decompilation and the examination of the application programming interface of terminal applications.

This research has found that the car-hailing platforms, which establish more constant personal data connectedness and connectivity with social media platforms, are taking the competitive market advantage within the sample network. Data access discrimination is a complementary method of market power in China's car-hailing industry.

This research offers a new perspective on the analysis of the multi-sided market from the personal data resource allocation mechanism of the car-hailing platform. However, the measurement of the data connectedness requires more empirical industry data.

This research reveals the competition structure that relies on personal data resource allocation mechanism. It offers empirical evidence for governance, which is considered as the critical issue of big data research, by reviewing the nature of the data network.

It also reveals the data convergence process of the social system and the technological system.

This research offers a new research method for the real-time regulation of the car-hailing platform.

The upper three OSI layers support the communication requirements of applications, while the lower four layers provide reliable transmission of data. This article describes the lower four layers. First, though, a brief overview of the layered model is presented including a summary of the upper three layers. Then a description of the lower three layers is followed by a discussion of data communication standards associated with specific layers. Architectural concepts are then explored: hierarchy and abstraction within the layers, levels of dialogue, internetworking, end‐to‐end communication, analysis of layer four, and a discussion of connection‐oriented, connectionless, and message‐oriented protocols and applications. The article concludes with a comparison of OSI and the de facto industry protocols, TCP/IP, which are currently used within the Internet.

This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.

The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.

Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.

To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.

This paper is an interim report of a study under way in the USA with the goal of developing a core set of national statistics and performance measures that librarians,researchers, and policy‐makers can use to describe public library and library‐based state‐wide network use of the Internet and Web‐based services and resources. The paper summarises preliminary findings and key issues identified as of January 2000. It describes a number of models for developing such statistics and performance measures. The paper also offers a number of preliminary statistics and performance measures that are being field‐tested to describe information resources and services in the networked environment. The authors expect to have a final set of such statistics and performance measures by the summer of 2000.

In the last decade, XML has become the de facto standard for data exchange in the world wide web (WWW). The positive benefits of data exchangeability to support system and software heterogeneity on application level and easy WWW integration make XML an ideal data format for many other application and network scenarios like wireless sensor networks (WSNs). Moreover, the usage of XML encourages using standardized techniques like SOAP to adapt the service‐oriented paradigm to sensor network engineering. Nevertheless, integrating XML usage in WSN data management is limited by the low hardware resources that require efficient XML data management strategies suitable to bridge the general resource gap. The purpose of this paper is to present two separate strategies on integrating XML data management in WSNs.

The paper presents two separate strategies on integrating XML data management in WSNs that have been implemented and are running on today's sensor node platforms. The paper shows how XML data can be processed and how XPath queries can be evaluated dynamically. In an extended evaluation, the performance of both strategies concerning the memory and energy efficiency are compared and both solutions are shown to have application domains fully applicable on today's sensor node products.

This work shows that dynamic XML data management and query evaluation is possible on sensor nodes with strict limitations in terms of memory, processing power and energy supply.

The paper presents an optimized stream‐based XML compression technique and shows how XML queries can be evaluated on compressed XML bit streams using generic pushdown automata. To the best of the authors' knowledge, this is the first complete approach on integrating dynamic XML data management into WSNs.

Little attention has been paid to restructuring existing massive amounts of literature data such that evidence-based meaningful inferences and networks be drawn therefrom. This paper aims to structure extant literature data into a network and demonstrate by graph visualization and manipulation tool “Gephi” how to obtain an evidence-based literature review.

The main objective of this paper is to propose a methodology to structure existing literature data into a network. This network is examined through certain graph theory metrics to uncover evidence-based research insights arising from existing huge amounts of literature data. From the list metrics, this study considers degree centrality, closeness centrality and betweenness centrality to comprehend the information available in the literature pool.

There is a significant amount of literature on any given research problem. Approaching this massive volume of literature data to find an appropriate research problem is a complicated process. The proposed methodology and metrics enable the extraction of appropriate and relevant information from huge quantities of literature data. The methodology is validated by three different scenarios of review questions, and results are reported.

The proposed methodology comprises of more manual hours to structure literature data.

This paper enables researchers in any domain to systematically extract and visualize meaningful and evidence-based insights from existing literature.

The procedure for converting literature data into a network representation is not documented in the existing literature. The paper lays down the procedure to structure literature data into a network.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.