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AI & SOCIETY 2022年第1期37卷 195-204页

作者： Fonseca, Fred Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

Although true in some aspects, the suggested characterization of today's science as a dichotomy between traditional science and data-driven science misses some of the nuance, complexity, and possibility that exists between the two positions. Part of the problem is the claim that data Science works without theories. There are many theories behind the data that are used in science. However, for data science, the only theories that matter are those in mathematics, statistics, and computer science. In this conceptual paper, we add two other philosophy of science tenets, experiments and data, to the discussion to create a more nuanced view of how data science uses theories. Following Ihde's concept of technoscience and the incessant quest for more precision, magnification, and resolution, we argue that technology-driven science created a need for more technology-driven science, culminating in data science. Further, we adapt Hacking and Galison's views on physics to argue that data science is also an experimental science, which uses data objects in experiments. Drawing from Heelan (The Journal of Philosophy 85:515-524, 1988), we called these objects "data-objects-for-knowing". Finally, we conclude that data science is a science to study artificially created phenomena-a science to study the data manipulated by the equations and operations of AI. It disregards the connections between data and the real world that were carefully built by the theories from other sciences. In the experiments of data science, data are the world itself. The knowledge created by data science is purposely disconnected from any theory from other sciences;it is a knowledge for the sake of itself.

关键词： data science data objects Heelan Theory Experimental science

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Prototype Mechanisms for Supporting the Network of Links to Parameterized data objects

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Procedia Computer Science 2021年 190卷 317-323页

作者： Larisa Ismailova Sergey Kosikov Viacheslav Wolfengagen National Research Nuclear University "Moscow Engineering Physics Institute" Kashirskoye Shosse 31 Moscow 115409 Russia NAO "JurInfoR" M. Pirogovskaya str. 5 Moscow 119435 Russia

The paper considers methods to organize a network of links to parameterized data objects. A data object is an object identified by standard methods, including the network identifiers URI, URL and URN. The data objects can be either atomic or have a complex structure, including links to other objects, and this ensures the linking of objects into a network, which can be considered as an information graph. It turns out to be possible to formulate a number of criteria for supporting the integrity of such a structure; the paper proposes language tools to formulate such criteria. An essential feature of this network is the dependence of the objects’ information content on a number of parameters, first of all, on time. Depending on the parameter, the information content of the object can change both with the preservation of the integrity of the structure, and with its damage. The paper considers methods for a parameterized description of objects while maintaining integrity based on the formalism of category theory. The formalization used allows defining two relations between objects and their names (identifiers): access and links. The paper studies the basic properties of such relations and reveals the differences between them. Category theoretical means of describing objects require appropriate support tools for practical application. The paper proposes a configuration of support tools for a network of links to parameterized data objects based on a combination of conceptual modeling tools and applicative computing tools. The proposed tools provide basic methods of access to the network of information graphs. An essential feature of the proposed tools is the ability to determine the subjective way of considering the information graph (based on the category of correlations), as well as harmonizing an objective and subjective way of considering. The proposed tools were tested in the development of applied information systems in the field of implementation of the best availab

关键词： data objects object identification conceptual constructions informational graph semantics category theory model parametrization

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Graphical Framework for Categorizing data Capabilities and Properties of objects in the Internet of Things

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IEEE ACCESS 2020年 8卷 22366-22377页

作者： Eduardo Ibarra-Esquer, Jorge Fernando Gonzalez-Navarro, Felix Salvador Sanchez, J. Leticia Flores-Rios, Brenda Angelica Astorga-Vargas, Maria Luisa Gonzalez-Ramirez, Maria Univ Autonoma Baja California Fac Ingn Mexicali 21280 Baja California Mexico Univ Autonoma Baja California Inst Ingn Mexicali 21280 Baja California Mexico Univ Jaume 1 Dept Comp Languages & Syst Inst New Imaging Technol Castellon de La Plana 12071 Spain

Things are the core of the Internet of Things (IoT) and must be properly characterized according to the different functions they accomplish. Identifying their capabilities and combining them as sets provides a view on the single or joint properties of existing things and guide in properly designing and building new things while maximizing their potential benefits within an IoT system or application. Building on five essential but independent capabilities of things (Identification, Localization, Sensing, Actuation, and Processing), four categories or groups of things are defined. These groups comprise a particular view of the diversity of objects found in the IoT, as trackable, data, interactive, or smart objects. In this paper, a description of the aforementioned capabilities is presented, stating how each of the groups of objects includes them. Then, given that data are the most important assets for both organizations and individuals a further description of the data objects group is made, proposing a graphical categorization framework that thoroughly describes and measures the level in which each of these capabilities is contained and how it contributes to the performance and data properties of any data object.

关键词： data objects data properties Internet of Things things capabilities categorization framework

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Modeling the data Object Routing in data Aware Networking 4th

Modeling the Data Object Routing in Data Aware Networking

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Devarakonda, G. K. Mohan Pilla, Sita Rama Murthy Bitra, Preethi Kumar, P. L. N. Prakash Durga, Poodi Venkata Vijaya Pitchai, R. Vishnu Institute of Technology Bhimavaram India Vishnu Institute of Technology Bhimavaram India Department of CSE Vishnu Institute of Technology Bhimavaram India Department of CSE B V Raju Institute of Technology Narsapur India

ISBN: (纸本)9783031488870

data Aware Networking (DAN) is one of the key technologies coined by the International Telecommunication for Technology. It aims to provide a system and method for managing data over the Internet. It also considers the needs of diverse users using the Internet and keeps track of their usage history to make the data proactive for them. Four key components of data Aware networking include the documents called data objects which are the focal point of attention, authors who create the documents, publishers who will maintain the documents along with a catalog of documents and access rights, the End users who will be utilizing the documents in a sophisticate way provided by the concept of DAN. Considering the architecture and the way the data objects are published in data-aware networking, this paper aims to suggest a dynamic and sophisticated routing system that will study the needs of diverse users and attempt to route the data either in a proactive or reactive manner to suit the needs of the users. This paper also proposes routing algorithms to be utilized in data-aware networking for both reactive and proactive routing. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： data Aware Networking data Driven Networking data objects Named data objects

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STATE: A Clustering Algorithm Focusing on Edges Instead of Centers

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Chinese Journal of Electronics 2021年第5期30卷 902-908页

作者： ZHAO Boxiang WANG Shuliang LIU Chuanlu School of Computer Science and Technology Beijing Institute of Technology Institute of E-Government Beijing Institute of Technology

With the expansion of data scale and the increase in data complexity, it is particularly important to accurately identify clusters and efficiently save clustering results. To address this, we propose a novel clustering algorithm, Shape clustering based on data field(STATE),which can quickly identify clusters of arbitrary shapes and greatly reduce the storage space of clustering results in any datasets without reducing the accuracy. STATE mainly focuses on finding the edges of clusters and directions of edges instead of clustering centers through the data *** results of STATE are presented as the edges of clusters without data objects inside clusters and without *** experiments show that STATE can recognize complex data distribution in noisy environments without discrimination and greatly save the storage space of clustering results. When it is applied in a real-world scene,facial feature extraction, STATE can recognize eyes, nose,mouth, eyebrows and facial contours automatically without calibrating key features or training. Using the extracted facial features, we achieve facial recognition with high accuracy.

关键词： clustering algorithm focusing storage space data complexity data field complex data distribution data objects data scale pattern clustering Shape clustering edges instead face recognition feature extraction novel clustering algorithm

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Research on big data anomaly mining method for power grid operation and maintenance based on reinforcement learning algorithm 9

Research on big data anomaly mining method for power grid op...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Wen, Xing CSG EHV Power Transmiss Co Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781665464215

As the scale of development of power grids continues to expand, the issue of their safe operation has received much attention. The problem of low accuracy in the face of network attacks exists in the big data anomaly mining method of grid operation and maintenance. A big data anomaly mining method of grid operation and maintenance based on reinforcement learning algorithm is designed. The method is based on a reinforcement learning algorithm, which is used to obtain early warning data, develop an offline analysis function, calculate an increase matrix of key nodes, construct a big data anomaly identification model and optimise the mining process. Experimental results: The accuracy of the designed mining method is 11.92%, 14.57% and 13.02% higher than the average of the other three methods.

关键词： Reinforcement learning algorithms Grid operations and maintenance Big data anomaly mining Grid security data objects

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From People to objects: The digital transformation of fields

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ORGANIZATION STUDIES 2022年第7期43卷 1091-1114页

作者： Alaimo, Cristina LUISS Univ Dept Polit Sci Res Digital Econ & Soc Rome Italy

Digital technologies are reconfiguring organizations and their environments. Activities are increasingly distributed across fields and coordinated by data, algorithms and machines. This paper investigates data objects (objects made of data structured and aggregated under a specific template) and their role in structuring fields and field practices. It studies programmatic advertising, an automated bidding process with hundreds of participants, whereby media spaces are auctioned in real time as individual users browse online content. To work on such a large scale, programmatic advertising must standardize existing field knowledge into data and coordinate collective action through objects, algorithms and technologies. This study shows how data, data objects and their infrastructures are involved in transforming the links between institutions and practice. The makeup and functioning of data and data objects reorient existing cognitive, normative and regulative structures, constrain the rule of engagement of actors and enable field-level autonomous interaction. The datafication of knowledge and automation of practices exposed in this paper call for a thorough rethinking of existing approaches to the concepts of organizations and fields.

关键词： algorithms automation data objects data institutional theory institutions organizational fields platforms technology

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The Construction of data Usability

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TECHNICAL COMMUNICATION QUARTERLY 2025年第2期34卷 199-217页

作者： Swarts, Jason North Carolina State Univ Raleigh NC USA

Technical communication aids in helping people envision the usability of technology. A similar need exists for constructing the usability of data sets, but the rhetorical work is less well documented. This article reports on how writers attribute meaning and usability to a geolocation data set and its derivative objects. This rhetorical work connects the data to existing and potential social realities, which provide a framework for thinking of that data set's usability.

关键词： data sets data objects user experience/usability/interaction design social construction

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PRACTICAL USE OF data objects IN R PROJECT

PRACTICAL USE OF DATA OBJECTS IN R PROJECT

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13th International Multidisciplinary Scientific Geoconference, SGEM 2013

作者： Stehlikova, Beata Horovcak, Pavel Tech Univ Kosice Inst Control & Informatizat Prod Proc Kosice Slovakia

ISBN: (纸本)9789549181890

The R project is open-source software which becomes a standard among statisticians and is widely used for statistical software development and data analysis. The paper is focused on the use of data objects in R project. Contribution characterizes data objects and deals with their usage. As the model data are used time entries of particular regimes of specified web service. The purpose is to present and describe ways to use object types in the program R.

关键词： R project data objects columns table structure

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data Quality Model-based Testing of Information Systems

Data Quality Model-based Testing of Information Systems

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Janis Bicevskis Zane Bicevska Anastasija Nikiforova Ivo Oditis Faculty of Computing University of Latvia Latvia DIVI Grupa Ltd Latvia

ISBN: (数字)9788395541674

ISBN: (纸本)9781728141473

This paper proposes a model-based testing approach by offering to use the data quality model (DQ-model) instead of the program's control flow graph as a testing model. The DQ-model contains definitions and conditions for data objects to consider the data object as correct. The study proposes to automatically generate a complete test set (CTS) using a DQmodel that allows all data quality conditions to be tested, resulting in a full coverage of DQ-model. In addition, the possibility to check the conformity of the data to be entered and already stored in the database is ensured. The proposed alternative approach changes the testing process: (1) CTS can be generated prior to software development; (2) CTS contains not only input data, but also database content required for complete testing of the system; (3) CTS generation from DQ-model provides values against which the system can be further tested. If the test results correspond to the values obtained during CTS generation, the system under test shall be considered to have been tested according to DQ-model. Otherwise, the user can verify the cause of the differences that may occur due incorrect software, as well as an inaccurate specification.

关键词： complete test set data quality model information system model-based testing symbolic execution. Computer based information systems data Quality CTS data objects scan-based testing computer software Test systems Clinical Test Result Social Conformity

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