Data Mining Services Research Articles

Research on Optimization of Communication Network Security and Intelligent Supervision Model in Urban Community Aging Adaptation Renovation

In the process of increasing the number of elderly people, their care and attention have received greater attention, which has made traditional community environments face insufficient data encryption performance and data mining services in the communication network security of aging communities. This has made communication security optimization and intelligent monitoring models in urban community aging adaptation and transformation a focus of research in the field of data communication security. This study delves into the improvement and enhancement of intelligent monitoring models for communication network security in urban communities. Its goal is to improve the security and intelligent monitoring mode of data communication networks during the transformation process through technological innovation and progress in management practices, and further improve the security performance of data communication. This research has solved the communication network security problems existing in intelligent devices and Internet services in the community aging transformation. Through the analysis of improvement strategies, this article compares the basic performance indicators of data encryption, firewall performance, data analysis and mining, and intelligent recommendation in communication network security and intelligent recommendation between different models. The optimization of communication network security in urban community aging adaptation can ensure the data security of terminal devices and further improve and enhance intelligent recommendation services for aging, providing more references and suggestions for communication network security and intelligent monitoring.

Big data application and corporate investment decisions: Evidence from A-share listed companies in China

Based on a natural language processing (NLP) method and annual reports from A-share listed companies in China from 2011 to 2020, this paper constructs a big data index for the corporate level and studies the impact of big data on corporate investment decisions. The study finds that big data significantly improves corporate investment decisions. From the perspective of index decomposition, data mining and mobile service are found to significantly improve corporate investment efficiency, while algorithm development and intelligent manufacturing mainly enhance the corporate investment level. The study also notes that heterogeneity may exist due to differences in financial, innovation, and legal environments. In terms of mechanisms, big data can positively influence corporate investment decisions by improving R&D investment and reducing transaction costs.

An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing

In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.

Pricing decisions of online and offline dual-channel supply chains considering data resource mining

Data resources, a fundamental component in the digital economy, play a vital role for businesses aiming to establish a lasting competitive edge. A company's data resources can uniquely influence the decisions surrounding products and services within the supply chain. The integrated dual-channel supply chain (DCSC) involves direct online channels utilized by manufacturing service providers alongside offline channels, such as physical retail stores, managed by sales service integrators. The DCSC is capable of performing second-stage data mining services in accordance with the sold products and customer services after the product sales are completed. Subsequently, data resource mining can be achieved through a cross-channel approach. As such, considering the exploration of cross-channel data resources, this study strives to formulate a structure for a dual-channel closed-loop supply chain. Furthermore, utilizing concepts from the Stackelberg and Nash equilibrium game theories, it delves into the analysis of pricing decisions and profit allocation. This examination encompasses distinct closed-loop supply chain configurations, viewed through the lenses of both centralized and decentralized decision-making approaches. In addition, the effects of cross-channel data mining and channel consumption preferences on supply chain decisions are analyzed, and the analysis is conducted in combination with numerical examples. As evidenced by the findings in this investigation, cross-channel data resource mining, consumer channel preference, and the data mining value conversion rate can prominently affect the formulation of pricing strategies and the distribution of profits in closed-loop supply chains. The potential value of data resources can lead to the generation of “external incentives” following the strategy of data resource mining. Furthermore, the data resource mining strategy is promising in stimulating the growth of the product and service markets. Finally, the overall profit of the supply chain is increased with the increase in the efficiency of data resource conversion. Enhancements in the efficacy of data resource conversion correspondingly lead to heightened overall profits within the supply chain.

Sentimental Approach to Airline Service Quality Evaluation

This paper focuses on the analysis of traditional methods of service quality evaluation and represents a new sentimental approach to airline service quality evaluation employing user-generated content. It identifies aspects of airline service that passengers react to positively or negatively using the word cloud method—a basic straightforward exploratory analysis tool. The aim is to introduce an approach that can be implemented using freely available analytical software tools and freely available data. As a case study, authors evaluated selected airlines’ service quality using sentimental analysis of user-generated content. The research relied on sentiment analysis of Twitter posts related to selected airlines’ service quality. The paper describes how Twitter can be used for data mining, sentimental analysis, and airline service quality evaluation. The authors analysed over 30,000 posts related to the service quality of Ryanair, Southwest Airlines, American Airlines and KLM and proposed two types of word clouds (for each individual airline) which allow more informed decisions about enhancing the service quality of selected airlines. Compared to rather expensive traditional methods of airline service quality evaluation, such as onboard surveys of airline passengers or on-site surveys of passengers at airport departure gates, the key advantages of this new approach are the availability of free data and free analytical software tools. Moreover, this approach allows analysis of the service quality of competing airlines and, thus, provides internal opportunities for comparison. The results contribute to the literature by clarifying how both positive and negative passenger feedback impacts airline service quality and airline product planning.

The landscape of research data management services in Malaysian academic libraries: librarians’ practices and roles

PurposeThe debate about academic librarians’ roles in research data management (RDM) services is currently relevant, especially in the context of making research data findable, accessible, interoperable and reproducible. This study aims to explore the RDM services offered by Malaysian academic libraries and the implementation progress based on the librarians’ practices and roles.Design/methodology/approachThis descriptive study involves three sequential forms of data collection: a website analysis of 20 academic libraries relating to RDM services, training and policy; an online survey of the academic libraries’ RDM implementation progress; and semi-structured interviews with three academic librarians to gauge their practices and roles in RDM services.FindingsMalaysian academic libraries provide RDM services based on their related or basic skills which are bibliographic management tools, institutional repository and openness of research data rather than impacted services to support RDM, such as data analysis, data citation, data mining or data visualisation services. Although the librarians were aware of RDM and their roles in research data services, the progress of practicing and implementation of the RDM services still has not been fully delivered to support the main RDM elements.Practical implicationsThis study illustrates the RDM roadmap on the current landscape of areas and types of services that the libraries are doing well. The list of services can be used and implemented as the best practices or strategies to be applied within Malaysian academic libraries.Originality/valueThis study highlights the gaps of RDM services in Malaysian academic libraries. To the best of the authors’ knowledge, as this is the first study in Malaysia that articulates the case of RDM services in academic libraries, it has paved the way for further research.

Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis.

BackgroundCurrent qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community.ObjectiveThis study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography.MethodsA data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results.ResultsA total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7% (27/105) were related to online community support, and 22% (23/105) focused on UTI risk factors and prevention strategies.ConclusionsOur large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women’s UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way.

Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining

In this article, an in-depth study and analysis of the precision marketing approach are carried out by building an IoT cloud platform and then using the technology of big data information mining. The cloud platform uses the MySQL database combined with the MongoDB database to store the cloud platform data to ensure the correct storage of data as well as to improve the access speed of data. The storage method of IoT temporal data is optimized, and the way of storing data in time slots is used to improve the efficiency of reading large amounts of data. For the scalability of the IoT data storage system, a MongoDB database clustering scheme is designed to ensure the scalability of data storage and disaster recovery capability. The relevant theories of big data marketing are reviewed and analyzed; secondly, based on the relevant theories, combined with the author's work experience and relevant information, a comprehensive analysis and research on the current situation of big data marketing are conducted, focusing on its macro-, micro-, and industry environment. The service model combines the types of user needs, encapsulates the resources obtained by the alliance through data mining for service products, and publishes and delivers them in the form of data products. From the perspective of the development of the telecommunications industry, in terms of technology, the telecommunications industry has seen the development trend of mobile replacing fixed networks and triple play. The development of emerging technologies represented by the Internet of Things and cloud computing has also led to technological changes in the telecommunications industry. Operators are facing new development opportunities and challenges. It also divides the service mode into self-service and consulting service mode according to the different degrees of users' cognition and understanding of the service, as well as proposes standardized data mining service guarantee from two aspects: after-sales service and operation supervision. A customized data mining service is a kind of data mining service for users' personalized needs. And the intelligent data mining service guarantee is proposed from two aspects of multicase experience integration and group intelligence. In the empirical research part, the big data alliance in Big Data Industry Alliance, which provides data mining service as the main business, is selected as the research object, and the data mining service model of the big data alliance proposed in this article is applied to the actual alliance to verify the scientific and rationality of the data mining service model and improve the data mining service model management system.

Review and prospect of research on ancient book information processing in China

The advent of the era of big data appears to be an unprecedented opportunity to the development of ancient book information processing in China. A comprehensive review of the research on ancient book information processing can help researchers understand the research progress and grasp the future research trend. Based on the life cycle of ancient book information processing, this paper reviews and summarizes the progress of digital resource construction, data mining, system construction and information service, and prospects the future research trend. Although progress has been made to some extent, the ancient book information processing is still in its infancy. It is expected that more researchers will pay attention to and engage in this field.

Social media analytics of overactive bladder posts: what do patients know and want to know?

To assess women's knowledge, patient experience, and treatment decision making regarding overactive bladder (OAB) using digital ethnography. Online posts were identified using a data mining service. Two hundred randomized posts were reviewed and coded using grounded theory. We then applied a latent Dirichlet allocation (LDA) probabilistic topic modeling process to review the entire collection of identified posts. A total of 2618 posts by 1867 unique users from 203 different websites were identified. Our analysis yielded six themes: the impact of OAB on quality of life, patient-physician interactions, online engagement, symptom management, patient knowledge acquisition, and alternative therapies. Overall, online communities are a source of support for women to self-manage the OAB symptom complex and help overcome treatment pathway challenges. Digital ethnography provides insight into patient knowledge and barriers to patient-centered care, which are important to improve patient outreach. Additionally, we identify similar findings to prior work, indicating the reliability of studying social media.

The Analysis of Internet Commercial Judicial Based on Big Data Alliance and Mining Service Process Model

At present, a series of economic structural changes created by the network economy have brought challenges to the entire economy and society. Traditional social commerce has also suffered severe tests under the background of network economy and global integration, and the rise and development of network commercial activities lack legal constraints. Based on the Big Data technology, in view of the characteristics of data mining services, this paper expands and changes the traditional model and proposes the Big Data alliance data mining service process model. Moreover, this paper uses intelligent decision‐making theory and knowledge reasoning methods to construct a fast‐responding, service‐reusable, and intelligent service model to realize the scalability of data mining services. In addition, this paper constructs system function modules according to the requirements of Internet commercial judicial data mining and verifies the performance of the system constructed in this paper through experimental analysis. The research results show that the system constructed in this paper has certain practical effects.

Female Lower Urinary Tract Symptom Prevention and Treatment Strategies on Social Media: Mixed Correlation With Evidence

ObjectiveTo evaluate the level of evidence behind recommendations on social media for disease prevention in five lower urinary tract symptoms. Materials and MethodsWe conducted a digital analysis of anonymous online posts on social media sites collected by a social media data mining service. One thousand posts about pelvic organ prolapse, stress urinary incontinence, overactive bladder, urinary tract infection, and interstitial cystitis/bladder pain syndrome were randomly selected. We analyzed these posts for recommendations regarding the prevention and treatment of these diseases, which were then compared to recommendations in available clinical guidelines and assessed for level of evidence. ResultsA total of 158 of 1000 posts contained 239 prevention strategies. For pelvic organ prolapse, there were 41 strategies identified, 25 (61%) of which had no evidence. For urinary tract infection 14 of 58 (29%) had no evidence, including recommendations for dietary modifications and urinary alkalization. For overactive bladder 8 of 28 (29%) had level 4 or no evidence. For stress urinary incontinence, 12 of 34 (36%) of prevention strategies had no evidence, such as laser rejuvenation and bladder training. Interstitial cystitis had the highest number of prevention strategies, and most were low or nonevidence based (70/79, 89%). ConclusionPrevention and treatment strategies are common in online discussions of pelvic floor disorders, but at least one third of these recommendations have no evidential support. There is a role for further online education and social media engagement by health care specialists to promote evidence-based practices.

MP31-02 IS DIGITAL ETHNOGRAPHY THE FOCUS GROUP OF THE FUTURE? FOCUS GROUPS VS. SOCIAL MEDIA ANALYSIS OF WOMENʼS EXPERIENCE WITH OVERACTIVE BLADDER (OAB)

INTRODUCTION AND OBJECTIVE: Qualitative methods assessing women’s perspectives on living with OAB have traditionally been obtained via focus groups and interviews. Now a plethora of cyber forums have emerged, allowing women to discuss their OAB experiences anonymously. We compared patient perceptions of OAB generated through focus groups to those gleaned from online social media. METHODS: For the digital ethnography analysis, 2,618 posts pertaining to OAB from 203 social media sites were identified by using keywords as search terms in a Java-based natural language processing platform and a social media data mining service. Of these, 200 posts were randomly selected for inclusion. The focus groups included were conducted previously (Anger et al, 2011). Women evaluated in urology clinic for OAB symptoms were recruited to participate in one of five focus groups, totaling 33 patients. Data from both methods were analyzed using grounded theory methodology. The data sets were coded independently and then compared. RESULTS: Each major theme that emerged from focus group data was echoed in the themes uncovered in analysis of social media posts. While major themes were generally similar, several unique subthemes were identified in each group (Table 1). The subthemes unique to the focus groups were centered on symptoms and management. The subthemes unique to the social media group were more personal, reflecting a sense of comfortability with sharing private insights anonymously. A new major theme was identified in the digital ethnography analysis: Online Community Engagement. The subthemes under this theme highlighted that women are pursuing self-education via online resources instead of relying on information from providers. CONCLUSIONS: This study highlights the reliability of qualitative data collected through social media compared to focus groups, as well as social media’s ability to procure personal, unbiased information. Digital ethnography provides immediate access to data on a large, diverse population, obviating the logistics required for focus groups. It presents an opportunity for providers to gain awareness of their patients’ perceptions, as well as what information their patients seek, facilitating a more effective patient-provider relationship.Source of Funding: n/a

Towards an Efficient Privacy-Preserving Decision Tree Evaluation Service in the Internet of Things

With the fast development of the Internet of Things (IoT) technology, normal people and organizations can produce massive data every day. Due to a lack of data mining expertise and computation resources, most of them choose to use data mining services. Unfortunately, directly sending query data to the cloud may violate their privacy. In this work, we mainly consider designing a scheme that enables the cloud to provide an efficient privacy-preserving decision tree evaluation service for resource-constrained clients in the IoT. To design such a scheme, a new secure comparison protocol based on additive secret sharing technology is proposed in a two-cloud model. Then we introduce our privacy-preserving decision tree evaluation scheme which is designed by the secret sharing technology and additively homomorphic cryptosystem. In this scheme, the cloud learns nothing of the query data and classification results, and the client has no idea of the tree. Moreover, this scheme also supports offline users. Theoretical analyses and experimental results show that our scheme is very efficient. Compared with the state-of-art work, both the communication and computational overheads of the newly designed scheme are smaller when dealing with deep but sparse trees.

Distributed data mining in grid computing environment

With the rapid development of computer technology, the data generated in the scientific research, industrial and commercial fields is increasing at an alarming rate. Traditional data mining techniques are limited to mining a single data source. How to mine distributed data sources and how to perform parallel mining is one of the hot topics in the field of data mining. The purpose of this article is to study distributed data mining in a grid computing environment. This paper studies the existing grid technology and data mining technology, and discusses the possibility of combining the two. Then based on this, a grid-based distributed data mining service framework is proposed, and the service framework is developed detailed design. This paper tests the framework, the experimental results show that applying the grid framework to distributed mining can improve the computing performance and data size. In this paper, the calculation speedup of the framework under 1 to 8 nodes is tested, and the speedup ratios are 1, 2, 3, 4, 5, 6, 7, and 8 respectively. It can be seen that the performance of the framework is directly proportional to the size of the calculation.

Data mining service recommendation based on dataset features

Quality of service (QoS)-based web service selection has been studied in the service computing community for some time. However, characteristics of the input dataset that is going to be processed by the web service are not usually considered in the selection process, even though they might have impact on QoS values of the service, e.g. latency on processing a bigger dataset is higher than that on a smaller dataset, one service takes longer time to process a certain dataset than another service. To address this issue, in this work, we take into consideration the dataset features in the QoS-based service recommendation process and we focus on data mining services because their QoS values could be highly dependent on dataset features. We propose two approaches for data mining service recommendations and compare their performances. In the first approach, we use a meta-learning algorithm to incorporate dataset features in the recommendation process and study the use of different machine learning algorithms (both classification models and regression models) as meta-learners in recommending data mining services for the given dataset. We also investigate the impact of the number of dataset features on the performance of the meta-learners. In the second approach, we propose a novel technique of using factor analysis for web service recommendation. We use decomposition technique to identify latent features of the input dataset and then recommend services by exploiting these latent variables. Our proposed approach of web service recommendation based on latent features was shown to be a more robust model with an accuracy of 85% compared to meta-feature-based recommendation.

Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things

With the popularization of Internet of Things (IOT) technology, a large number of multi-source heterogeneous data are constantly generated and collected by cloud platforms, which indicates that the problem of large data in IOT has become increasingly prominent, especially for massive tags and information in IOT which is urgent to use appropriate data mining algorithms to mine the value of these data. A collaborative filtering recommendation algorithm based on multi-information source fusion (CFR-MIF) is proposed where a feature vector and time weight function are introduced to improve the accuracy of top-N recommendation. It can conveniently and effectively process the IoT data, and furthermore integrate, manage and store the massive data collected from different industries and data formats. Besides, It also provides data mining services in the whole IoT realizing prediction and decision-making, which reverses control these sensor networks, so as to control the movement and development process of objective in the Internet of Things. The experimental results based on DeviceLens 1M data set show that the proposed algorithm greatly improves the accuracy of recommendation results, recall rate and F1 value compared with other advanced algorithms.

Development of BPMN Architecture using Integration of Data Mining

Data mining is an important factor of success for modern business processes. Modern Integrating data mining with business solutions will improve the business process model (BPM) and enhance the overall business process management framework. The proposed architecture promises to provide great support for flexible design, deployment and managementof business processes. Incorporating services of data mining in order to choose the appropriate business model, determining missing standards and deploying machine learning techniques in an applicable manner is a challenging task discussed in the paper. Theproposed work describes the overall contribution of data mining services and validates the novelty in architecture by defining user roles for business, decision mining and IT standards. Supervised and unsupervised learning technique is used for determining similarity between the proposed model and the previously stored models.

Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes

Internet of Things (IoT) technology provides a competent and structured approach to handle service deliverance aspects of healthcare in terms of mobile health and remote patient monitoring. IoT generates an unprecedented amount of data that can be processed using cloud computing. But for real-time remote health monitoring applications, the delay caused by transferring data to the cloud and back to the application is unacceptable. Relative to this context, we proposed the remote patient health monitoring in smart homes by using the concept of fog computing at the smart gateway. The proposed model uses advanced techniques and services, such as embedded data mining, distributed storage, and notification services at the edge of the network. Event triggering-based data transmission methodology is adopted to process the patient’s real-time data at fog layer. Temporal mining concept is used to analyze the events adversity by calculating the temporal health index of the patient. In order to determine the validity of the system, health data of 67 patients in IoT-based smart home environment was systematically generated for 30 days. Results depict that the proposed Bayesian belief network classifier-based model has high accuracy and response time in determining the state of an event when compared with other classification algorithms. Moreover, decision making based on real-time healthcare data further enhances the utility of the proposed system.

Search Engine, Inc.: Growth Versus Exit Strategy for a Mature Tech Start-Up

This case is about an information technology start-up that has grown into a global tech company in 10 years with revenue growth of 35.4% from 2008 to 2011. However, during that same period, four major competitors were acquired by integrated search providers like Microsoft and Oracle. These firms integrate enterprise search into broader information services like data mining, analytic, and predictive services. This case enables students to role-play as a consulting team hired by Search Engine, Inc.’s Board to assess the company’s strengths and weaknesses relative to its changing competitive environment and to evaluate whether industry consolidation should be viewed as a threat to its long-term viability or as an opportunity for further growth. If the team recommends selling the company, it needs to identify a potential buyer who would benefit the most from synergistic effects of acquiring their technology, talents, and clients. If the team recommends a growth strategy, it has to specify whether the company should grow within the specialized search market or compete in the integrated search market and whether to finance such growth with private equity or with an IPO. Whether a growth or exit strategy is recommended, the team needs to come up with a fair market valuation.