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检索条件"主题词=Data Engineering"

共 13097 条记录，以下是111-120 订阅

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Reverse data engineering of E-R-designed relational schemas

Reverse data engineering of E-R-designed relational schemas

引用

International Conference on databases, Parallel Architectures and Their Applications

作者： F. Springsteel C. Kou Computer Science Department University of Missouri Columbia MO USA

A novel solution is presented for the data engineer's inverse mapping problem: to construct from a relational database schema (RDBS) a corresponding entity-relationship diagram (ERD). The inverse mapping is difficult because many ERDs may correspond to one ERDs, or to none if the schema is not well formed, so it is not clear how to define a mapping in this direction. Nonetheless, it would be desirable to choose the most representative ERD whenever possible, so that the benefits of E-R visualization and analysis can be applied to RDBSs, even after their relation schemes have been changed. The authors present a first approach to an experimental solution for this inversion problem, for the case of an RDBS that was originally designed by an ERD-based algorithm, which means that it was once a canonical relational schema (CRS) and subsequently altered. The demonstration system tracks each 'atomic change' in the CRS and determines, by means of its PROLOG-implemented E-R knowledge base, the corresponding changes in the given ERD. The reasonable restrictions that the present system puts on the legal set of possible changes, for example, on deletions, allow it to trace their effects correctly, so that well-formedness of the ERD is preserved.< >

关键词： data engineering Relational databases Testing Prototypes Spatial databases Computer science Visualization Algorithm design and analysis Law Legal factors

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Optimizing Security Operations: Hybrid Intrusion Detection Systems Leveraging data engineering

Optimizing Security Operations: Hybrid Intrusion Detection S...

引用

Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： T. Sharath A. Muthukumaravel Department of Computer Science Bharath Institute of Higher Education and Research Chennai India Department of Arts and Science Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Traditional intrusion detection systems (IDS) incapacity to keep up with the ever-evolving cyber threats is the reason for their poor threat identification skills and high false positive rates. A hybrid intrusion detection system (HIDS) employing existing data engineering methods is proposed in the study as a remedy for these issues. To provide an integrated method of threat prevention, the HIDS blends machine learning (ML) driven anomaly detection with signature-based identification. Using a sequence of procedures including data collecting, preprocessing, feature selection, model training, and real-time monitoring, the HIDS lowers false positive rates, boosts computation efficiency, and enhances detection accuracy. The proposed system outperforms the existing system according to results and analysis; it has higher accuracy (0.95), lower false positive and false negative rates (0.03), better detection rates across a range of attack types, and increased computational efficiency (30% CPU usage, 500 MB memory consumption, 50 ms detection speed). Overall, the HIDS provides companies with robust defenses against a range of cyber threats, demonstrating a proactive and adaptable attitude to cybersecurity.

关键词： Training Accuracy Prevention and mitigation Intrusion detection Training data data engineering Feature extraction Computational efficiency Computer security Anomaly detection

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Efficient Threat Detection Machine Learning Techniques for Intrusion Detection Utilizing data engineering

Efficient Threat Detection Machine Learning Techniques for I...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study presents a novel methodology for enhancing intrusion detection systems (IDS) through the integration of state-of-the-art data engineering techniques and machine learning approaches. Traditional intrusion detection systems are facing increasing challenges in keeping pace with the continuously evolving complexity of cyber-attacks. By means of rigorous data preparation, feature extraction, and model modification, The proposed work augments the scalability and precision of threat detection methodologies. Through the integration of machine learning and data engineering, the methodology presents a potential resolution to the obstacle of implementing effective threat detection in the realm of network security. By means of comprehensive testing and evaluation, this showcases substantial enhancements in the accuracy of detection and the overall performance of the system. Considering the critical necessity for proactive measures in countering perpetually evolving cyber threats, this research contributes significantly to the growing body of knowledge regarding cybersecurity defenses. The SVM achieves accuracy of 95%.

关键词： Support vector machines Accuracy Scalability Intrusion detection Machine learning Organizations data engineering Threat assessment Testing Resilience

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Metadata-Oriented data engineering Approach for Integration of Medical data: A Pilot Study

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JOURNAL OF THORACIC ONCOLOGY 2022年第9期17卷 S313-S314页

作者： Kahya, Y. Yilmaz, K. C. Gorguner, F. Kocaman, G. Yenigun, B. M. Yuksel, C. Yilmaz, A. E. Cangir, A. Kayi Ankara Univ Fac Med Ankara Turkey Ankara Univ Fac Engn Ankara Turkey

EP07.01-0

关键词： data engineering data mining Metadata

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Position paper: BE(Hons) data engineering

Position paper: BE(Hons) data engineering

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International Conference on Information Technology Based Higher Education and Training, ITHET

作者： Robin Braun Wayne Brookes Zenon Chaczko Roger Hadgraft University of Technology Sydney Sydney NSW AU

ISBN: (纸本)9781509007790

This paper describes the UTS view of data engineering, and the degree program that has been created to educate data Engineers. The document describes the guiding philosophies and the key ideas used in designing the program. The design of the program takes advantage of the 3rd, 4th and 5th shifts in engineering education[1]. Namely the shift to emphasizing design (problem solving) (3), the shift to applying education, learning, and social-behavioral sciences research, and the shift to integrating information, computational, and communications technology in education. Mills and Treagast[2] question the relative value of Problem Based or Project Based learning in the Australian context. They quote a comparison of problem-based and project-based learning at tertiary level by Perrenet et al[3]. They noted that the similarities between the two strategies are that they are both based on self-direction and collaboration, and that they both have a multidisciplinary orientation.

关键词： data engineering Contracts Spirals Technological innovation Australia Computers Entrepreneurship

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Open sourcing education for data engineering and data Science 46th

Open sourcing education for Data Engineering and Data Scienc...

引用

Frontiers in Education (FIE) Conference

作者： David E Drummond Data Engineering Insight Data Engineering Palo Alto CA

ISBN: (纸本)9781509017911

The fields of data engineering and data Science have emerged in recent years as an exciting intersection between leading academic research and industry leaders, and include diverse, cutting-edge topics like distributed systems, machine learning, and artificial intelligence. While these topics are interesting in their own right, perhaps the most compelling aspect of these fields is how the tools, which are in widespread use, have been developed by an Open Source community. Individuals across several universities and companies have worked together in a distributed fashion to build and improve the leading generation of data technologies. These Open Source principles have enabled the latest industry-adopted tools to constantly evolve at an incredible rate. One way for educators to keep pace with these developments and maintain advanced curriculum is to adopt the same collaborative principles used in open source. At the Insight data Fellowship, we've used this open source model to provide immediate feedback and drive our curriculum forward, while fostering a culture of independence and curiosity. This session will show engineering educators how to use open source principles and tools to develop their own curriculum.

关键词： data engineering Industries Collaboration Sparks Education Learning (artificial intelligence)

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Q-Filter computational structures for paradigm shifts in data engineering

Q-Filter computational structures for paradigm shifts in dat...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Magdi A. Mohamed Weimin Xiao Physical Realization Research CoE Motorola Research Laboratories Schaumburg IL USA

An advanced approach for adaptive nonlinear digital data processing is described in this article. Three primal computational structures referred to as Q-Measures, Q-Metrics, and Q-Aggregates are introduced and utilized in unison as highly adaptive data analysis handlers. The proposed approach relies on universal functionals using few parameters to characterize dynamic system behaviors in broad ranges of unconventional measure, metric, and aggregation spaces. We present this unique approach in application to real-valued signal processing tasks, with suitable optimization algorithms, so that the parameters of the proposed models can be tuned automatically. The new approach is tested on real data sets and the experiments show promising results.

关键词： data engineering data analysis Integral equations data processing Signal processing algorithms Hardware data mining USA Councils Computational intelligence Associate members

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Building trustworthy smart cities: a systems engineering approach to data engineering at the Smart Metrology Campus

Building trustworthy smart cities: a systems engineering app...

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International Conference on data engineering Workshops

作者： Michael Benedict Ulbig Daniel Hutzschenreuter Barbara Jung Metrology for Digital Transformation Physikalisch-Technische Bundesanstalt Braunschweig Germany

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

This paper introduces the concept of a Smart Metrology Campus, with its primary objective being to facilitate reliable data science in the realm of smart cities. This is achieved by connecting sensor and meter data with metadata, particularly from the field of metrology. The establishment of a robust data infrastructure, responsible for collecting this data, requires a strategic data engineering approach. The challenge lies in identifying a data engineering approach that is well-suited for design considerations characterized by high demands in scale, complexity, sensitivity and reliability. To address this challenge, the paper conducts a comprehensive analysis of the applicability of existing guides for data engineering, alongside other guiding processes from related disciplines such as data mining, data science, and systems engineering. As a result, systems engineering is identified as highly relevant approach for a data engineering meeting the needs of smart cities. The subsequent sections delve into the exploration of how our approach can be utilized and adapted for the specific requirements of a data engineering process in this context.

关键词： Sensitivity Smart cities Metrology data science data engineering Systems engineering and theory Reliability engineering

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High Performance Computing: From Deep Learning to data engineering

High Performance Computing: From Deep Learning to Data Engin...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Geoffrey Fox Indiana University USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

We describe how High-Performance Computing (HPC) can be used to enhance Big data and Machine Learning (ML) systems (HPC for ML) but also how machine learning can be used to enhance system execution (ML for HPC). We review the different aspects of data engineering needed to process large scale data and how it is implemented in the Twister2 system combined with PyTorch and TensorFlow. We discuss the different forms of parallelism seen in deep learning with a focus on pipelined parallelism over layers.

关键词： Machine learning Physics Parallel processing data engineering Informatics Computer science Cloud computing

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High Performance data engineering Everywhere

High Performance Data Engineering Everywhere

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Smart data Services (SMDS), IEEE International Conference on

作者： Chathura Widanage Niranda Perera Vibhatha Abeykoon Supun Kamburugamuve Thejaka Amila Kanewala Hasara Maithree Pulasthi Wickramasinghe Ahmet Uyar Gurhan Gunduz Geoffrey Fox Digital Science Center Bloomington IN USA Luddy School of Informatics Computing and Engineering IN USA Indiana University Alumni IN USA University of Moratuwa Sri Lanka

ISBN: (数字)9781728187778

ISBN: (纸本)9781728187785

The amazing advances being made in the fields of machine and deep learning are a highlight of the Big data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide. However this is just a small part of the issues facing the overall data processing environment, which must also support a raft of data engineering for pre- and post-data processing, communication, and system integration. An important requirement of data analytics tools is to be able to easily integrate with existing frameworks in a multitude of languages, thereby increasing user productivity and efficiency. All this demands an efficient and highly distributed integrated approach for data processing, yet many of today's popular data analytics tools are unable to satisfy all these requirements at the same time. In this paper we present Cylon, an open-source high performance distributed data processing library that can be seamlessly integrated with existing Big data and AI/ML frameworks. It is developed with a flexible C++ core on top of a compact data structure and exposes language bindings to C++, Java, and Python. We discuss Cylon's architecture in detail, and reveal how it can be imported as a library to existing applications or operate as a standalone framework. Initial experiments show that Cylon enhances popular tools such as Apache Spark and Dask with major performance improvements for key operations and better component linkages. Finally, we show how its design enables Cylon to be used cross-platform with minimum overhead, which includes popular AI tools such as PyTorch, Tensorflow, and Jupyter notebooks.

关键词： data analysis C++ languages System integration Tools Big data data engineering Libraries

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