Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture Recognition Case Study

Grzegorowski, Marek; Stawicki, Sebastian

doi:10.15439/2015f425

Cited by 13 publications

(6 citation statements)

References 30 publications

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“… Extracting Features: Unstructured and continuous data streams in big data systems require considerable effort, and therefore, feature extraction methods are used to separate useful and structured data from raw big data. Depending on the nature and type of data, various statistical methods are used to identify time-domain and frequency domain features from big data (Grzegorowski & Stawicki, 2015).…”

Section: Big Data Collectionmentioning

confidence: 99%

Big data reduction framework for value creation in sustainable enterprises

Rehman

Chang

Batool

et al. 2016

International Journal of Information Management

212

140

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Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-toend data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications.

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Section: Big Data Collectionmentioning

confidence: 99%

Big data reduction framework for value creation in sustainable enterprises

Rehman

Chang

Batool

et al. 2016

International Journal of Information Management

212

140

View full text Add to dashboard Cite

show abstract

“…Furthermore, we plan to consider changes in users' behavior and preferences by periodically updating frequent itemsets based on recent changes in rating history. It would also be of value to extend the users' and items' data representation by applying a more advanced feature extraction to model the similarities among them more effectively [44], [45], [46].…”

Section: Discussionmentioning

confidence: 99%

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Kannout¹,

Grodzki²,

Grzegorowski³

2022

Annals of Computer Science and Information Systems

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Although several approaches have been proposed throughout the last decade to build recommender systems (RS), most of them suffer from the cold-start problem. This problem occurs when a new item hits the system or a new user signs up. It is generally recognized that the ability to handle cold users and items is one of the key success factors of any new recommender algorithm. This paper introduces a frequent pattern mining framework for recommender systems (FPRS) -a novel approach to address this challenging task. FPRS is a hybrid RS that incorporates collaborative and content-based recommendation algorithms and employs a frequent pattern (FP) growth algorithm. The article proposes several strategies to combine the generated frequent itemsets with content-based methods to mitigate the cold-start problem for both new users and new items. The performed empirical evaluation confirmed its usefulness. Furthermore, the developed solution can be easily combined with any other approach to build a recommender system and can be further extended to make up a complete and standalone RS.Index Terms-recommendation system, cold-start problem, frequent pattern mining, quality of recommendations.

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“…Features extracted from the dataset are based on IMU and heart rate and skin temperature observations. The approach of feature extraction has been carried out similar to the other studies on activity recognition [40,41]. The feature extraction is performed by defining windows on the dataset attributes as shown in Figure 4.…”

Section: Proposed Design Methodologymentioning

confidence: 99%

Design Methodology of Microservices to Support Predictive Analytics for IoT Applications

Ali

Jarwar

Chong

2018

Sensors

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In the era of digital transformation, the Internet of Things (IoT) is emerging with improved data collection methods, advanced data processing mechanisms, enhanced analytic techniques, and modern service platforms. However, one of the major challenges is to provide an integrated design that can provide analytic capability for heterogeneous types of data and support the IoT applications with modular and robust services in an environment where the requirements keep changing. An enhanced analytic functionality not only provides insights from IoT data, but also fosters productivity of processes. Developing an efficient and easily maintainable IoT analytic system is a challenging endeavor due to many reasons such as heterogeneous data sources, growing data volumes, and monolithic service development approaches. In this view, the article proposes a design methodology that presents analytic capabilities embedded in modular microservices to realize efficient and scalable services in order to support adaptive IoT applications. Algorithms for analytic procedures are developed to underpin the model. We implement the Web Objects to virtualize IoT resources. The semantic data modeling is used to promote interoperability across the heterogeneous systems. We demonstrate the use case scenario and validate the proposed design with a prototype implementation.

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Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture Recognition Case Study

Cited by 13 publications

References 30 publications

Big data reduction framework for value creation in sustainable enterprises

Big data reduction framework for value creation in sustainable enterprises

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Design Methodology of Microservices to Support Predictive Analytics for IoT Applications

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