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Abstract

With the increasing amount of digital journal submissions, there is a need to deploy new scalable computational methods to improve information accessibilities. One common task is to identify useful information and named entity from text documents such as journal article submission. However, there are many technical challenges to limit applicability of the general methods and lack of general tools. In this paper, we present domain informational vocabulary extraction (DIVE) project, which aims to enrich digital publications through detection of entity and key informational words and by adding additional annotations. In a first of its kind to our knowledge, our system engages authors of the peer-reviewed articles and the journal publishers by integrating DIVE implementation in the manuscript proofing and publication process. The system implements multiple strategies for biological entity detection, including using regular expression rules, ontology, and a keyword dictionary. These extracted entities are then stored in a database and made accessible through an interactive web application for curation and evaluation by authors. Through the web interface, the authors can make additional annotations and corrections to the current results. The updates can then be used to improve the entity detection in subsequent processed articles in the future. We describe our framework and deployment in details. In a pilot program, we have deployed the first phase of development as a service integrated with the journals Plant Physiology and The Plant cell published by the American Society of Plant Biologists (ASPB). We present usage statistics to date since its production on April 2018. We compare automated recognition results from DIVE with results from author curation and show the service achieved on average 80% recall and 70% precision per article. In contrast, an existing biological entity extraction tool, a biomedical named entity recognizer (ABNER), can only achieve 47% recall and return a much larger candidate set.

Abstract

The emerging transdiscipline of Computational Archival Science (CAS) links frameworks such as Brown Dog and repository software such as Digital Repository At Scale To Invite Computation (DRAS-TIC) to yield an understanding of working with digital collections at scale for cultural data. The DRAS-TIC and Brown Dog projects here serve as the basis for an expandable distributed storage/service architecture with on-demand, horizontally scalable integrated digital preservation and analysis services.

Abstract

With more and more users using different devices, such as personal computers, iPads, and smartphones, they can access OPAC (online public access catalog) services and other digital library services in different contexts. This leads to the phenomenon that user’s behavior can be transferred to different devices, which leads to the richness and diversity of user’s behavior data in digital libraries. A large number of user data challenge digital libraries to analyze user’s behavior, such as search preferences and borrowing habits. In this study, we study the user’s cross-device transition behavior when using OPAC. Based on the large-scale OPAC transaction log, the online activities between device transitions in the process of using OPAC are studied. In order to predict the follow-up activities that users may take, and the next device that users may use, we detect features from several perspectives and analyze the feature importance. We find that the activity and time interval on the first device are more important for predicting the user’s next activity and the next device. In addition, features of operating system help to better predict the next device. The next device used is more likely to predict the next activity after the device transition. This study examines the cross-device transition prediction in library OPAC, which can help libraries provide smart services for users when accessing OPAC on different devices.

Abstract

Open science is prompting wide efforts to make data from research available for broader use. However, sharing data is complicated by important protections on the data (e.g., protections of privacy and intellectual property). The spectrum of options existing between data needing to be fully open access and data that simply cannot be shared at all is quite limited. This paper puts forth a generalized remote secure enclave as a socio-technical framework consisting of policies, human processes, and technologies that work hand in hand to enable controlled access and use of restricted data. Based on experience in implementing the enclave for computational, analytical access to a massive collection of in-copyright texts, we discuss the synergies and trade-offs that exist between software components and policy and process components in striking the right balance between safety for the data, ease of use, and efficiency.

Abstract

The concept of Big Data is popular in a variety of domains. The purpose of this review was to summarize the features, applications, analysis approaches, and challenges of Big Data in health care. Big Data in health care has its own features, such as heterogeneity, incompleteness, timeliness and longevity, privacy, and ownership. These features bring a series of challenges for data storage, mining, and sharing to promote health-related research. To deal with these challenges, analysis approaches focusing on Big Data in health care need to be developed and laws and regulations for making use of Big Data in health care need to be enacted. From a patient perspective, application of Big Data analysis could bring about improved treatment and lower costs. In addition to patients, government, hospitals, and research institutions could also benefit from the Big Data in health care.

Abstract

This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

Abstract

The concept of Big Data is popular in a variety of domains. The purpose of this review was to summarize the features, applications, analysis approaches, and challenges of Big Data in health care. Big Data in health care has its own features, such as heterogeneity, incompleteness, timeliness and longevity, privacy, and ownership. These features bring a series of challenges for data storage, mining, and sharing to promote health-related research. To deal with these challenges, analysis approaches focusing on Big Data in health care need to be developed and laws and regulations for making use of Big Data in health care need to be enacted. From a patient perspective, application of Big Data analysis could bring about improved treatment and lower costs. In addition to patients, government, hospitals, and research institutions could also benefit from the Big Data in health care.

Abstract

Mobile devices are gaining popularity among online shoppers whose behavior has been reshaped by the changes in screen size, interface, functionality, and context of use. This study, based on a log file from a cross-border E-commerce platform, conducted a clickstream data analysis to compare desktop and mobile users’ visiting behavior. The original 2,827,449 clickstream records generated over a 4-day period were cleaned and analyzed according to an established analysis framework at the footprint level. Differences are found between desktop and mobile users in the distribution of footprints, core footprints, and footprint depth. As the results show, online shoppers preferred to explore various products on mobile devices and read product details on desktops. The E-commerce mobile application (app) presented higher interactivity than the desktop and mobile websites, thus increasing both user involvement and product visibility. It enabled users to engage in the intended activities more effectively on the corresponding pages. Mobile users were further divided into iOS and Android users whose visiting behaviors were basically similar to each other, though the latter might experience slower response speed.