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About 10,000 papers for “Data classification”

The Data Classification solutions provide the mechanisms needed to categorize and tag content across all the compliance solutions in Microsoft 365. Microsoft designed the compliance center's solutions to govern, protect, and search for information in different ways, depending on your business requirements. The Data Classification solutions provide the foundational features across all the solutions.

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Data Classification

Auerbach Publications eBooks · 2004

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Data Classification

Dr Reza Alavi · 2025

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Data Classification

2017

This chapter discusses various architectural concepts and design requirements including cloud data lifecycle, data discovery, data retention policies, data event logging, and internal and external audit controls. The data owner is in the best position to understand how the data is going to be used by the organization. This allows the data owner to appropriately categorize the data. The organization might want to have specific categories for different uses of data. Functional Unit Each department or office might have its own category, and keep all data it controls within its own category. Data is assigned a classification according to the sensitivity of the data, based on the negative impact an unauthorized disclosure would cause. Various legal constructs exist across the globe, and being in the cloud means the organization may be subject to many simultaneously. Libraries and archives are allowed to make limited numbers of copies of original works in order to preserve the work itself. A trademark is meant to protect the esteem and goodwill that an organization has built among the marketplace, especially in public perception. Anyone other than the owner of the trade secret who discovers or invents the same or similar methods, processes, and information through legal means is justified and legally free to use that knowledge to their own benefit.

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Data Classification

2019

This chapter discusses how data is categorized and classified, why the location of the data matters, intellectual property concepts and practices, and various aspects of data retention and deletion requirements. Data is an asset. As with all other assets, knowing what data you have, where it is (logically and physically), and its relative value and need for protection is absolutely essential for properly apportioning security resources. The data owner is the organization that has collected or created the data, in general terms. Within the organization, we often assign a specific data owner as being the individual with rights and responsibilities for that data; this is usually the department head or business unit manager for the office that has created or collected a certain dataset. The data custodian is any person or entity that is tasked with the daily maintenance and administration of the data. The custodian also has the role of applying the proper security controls and processes as directed by the data owner. Within an organization, the custodian might be a database administrator. It is important to know the phases of the data lifecycle, in order, both for the CCSP exam and for your understanding of data security concepts. In this section of the chapter, we're mostly concerned with the first phase: Create. The data owner will be identified in the Create phase. Many data security and management responsibilities require action on the part of the data owner at this point of the lifecycle.

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Data Classification

Michael Paluszek, Stephanie Thomas · Apress eBooks · 2016

The remainder of the book provides machine learning examples in MATLAB that span the technologies discussed. Each example provides a useful application in its own right. Full source code is provided. In each case the theory behind the code is provided. References for further study are provided. Each example is self-contained and addresses one of the autonomous learning technologies discussed earlier in the book. You can jump around and try the examples that interest you the most.

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Data classification criteria.

Fang Han (435237), Shengtai Zhang (14227667), Junpeng Yuan (13913754) et al. · Figshare · 2022

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Data Classification Analysis

Juan Villacrés, Fernando Auat Cheein · 2023

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Data Models Classification

Sadagopan Parthasarathy · 2014

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Tissue Classification Data

Julio Villalón · Figshare · 2016

This data is part of the DIPY (Diffusion Imaging in Python) Tissue Classification project. It is a T1-weighted MRI image used as an example file to perform tissue classification and separate between tissue types. The T1-weighted image comes in a raw and a denoised version. It also contains an Anisotropic Power map from the same subject for the purpose of also testing segmentation algorithms on it. Total of three nifti images.

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Raw classification data.

Lisa Ekman (5626265), Elisabeth Bagge (18370), Ann Nyman (3422198) et al. · Figshare · 2020

The results of the classification of the sequenced reads (after removal of sequences assigned to the Bos Taurus genome) presented in an Excel table as both clade read counts and normalized into a percentage of classified reads for each taxa. (XLSX)

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Data Classification Methods

Pavol Kaščák · Digital Repository (National Repository of Grey Literature)

This bachelor's thesis deals with data classification, focusing on the implementation of naive Bayes classification method. At First, it is generally described process of data classification, its division into phases with their characteristic. It is followed by a more accurate description of the naive Bayes classification method and description of the implementation by using Java programming language and MySQL database. The last section contains a summary of the results.

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Binary data classification.

Jean Daunizeau (195606), Vincent Adam (5657632), Lionel Rigoux (304110) · Figshare · 2015

This figure exemplifies a classification analysis, which is used to infer on the link between a continuous variable X and a binary data y. The analysis is conducted on data simulated under either a null model (H0: no link) or a sigmoid mapping (H1). Upper left: the classification accuracy, in terms of the Monte-Carlo average probability of correct prediction under both types of data (left: H1, right: H0), for the training dataset. The green dots show the expected classification accuracy, using the true values of each model's set of parameters. The dotted red line depicts chance level. Upper right: same format, test dataset (no model fitting). Lower left: same format, for the log Bayes factor , given the training dataset. Lower right: same format, given the full (train+test) dataset.

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Linked data classification

Alfio Ferrara, Lorenzo Genta, Stefano Montanelli · 2013

The availability of large collections of linked data that can be accessed through public services and search endpoints requires methods and techniques for reducing the data complexity and providing high-level views of data contents defined according to users specific needs. To this end, a crucial step is the definition of data classification methods and techniques for the thematic aggregation of linked data. In this paper, we propose matching and clustering techniques specifically conceived for linked data classification, by focusing on the high level of heterogeneity of data descriptions in terms of the number and kind of their descriptive features.

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Atomization classification data

Jin, Dongdong · Figshare · 2025

雾化分类图像包括六大类：喷雾角度过小的图像3610张、喷雾角度过大的图像3599张、漏液图像1478张、模糊图像5461张、正常喷雾图像5917张、无喷涂图像4703张

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Telugu Data Classification

Komal Parashar · International Journal for Research in Applied Science and Engineering Technology · 2024

Abstract: Telugu is considered as the difficult languages which is morphologically rich when it comes to Dravidian languages. There are many Telugu documents available on Internet, it is important to organize the data by automatically by assigning a collection of text with predefined categories. Here the Telugu data is classified into multiple areas like business, sports, entertainment, nation, editorial is the main goal throughout this research work. This research work provides up an efficient model by adopting some ML classifiers such as SVM, Naive Bayes, and Logistic regression to perform some areas of classification on Telugu data. The results obtained by various machine-learning models are compared and an efficient model is discovered, and it is observed that the Naive Bayes model outperformed with reference to accuracy, precision, recall, and F1- score

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Big data classification

Shan Suthaharan · ACM SIGMETRICS Performance Evaluation Review · 2014

This paper focuses on the specific problem of Big Data classification of network intrusion traffic. It discusses the system challenges presented by the Big Data problems associated with network intrusion prediction. The prediction of a possible intrusion attack in a network requires continuous collection of traffic data and learning of their characteristics on the fly. The continuous collection of traffic data by the network leads to Big Data problems that are caused by the volume, variety and velocity properties of Big Data. The learning of the network characteristics require machine learning techniques that capture global knowledge of the traffic patterns. The Big Data properties will lead to significant system challenges to implement machine learning frameworks. This paper discusses the problems and challenges in handling Big Data classification using geometric representation-learning techniques and the modern Big Data networking technologies. In particular this paper discusses the issues related to combining supervised learning techniques, representation-learning techniques, machine lifelong learning techniques and Big Data technologies (e.g. Hadoop, Hive and Cloud) for solving network traffic classification problems.

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Data Classification Guidelines

M Kelly Timothy · 2016

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Descriptive classification data.

Sebastian J. Theobald (6001607), Christoph Kreer (618139), Sahamoddin Khailaie (6001610) et al. · Figshare · 2020

Cited by 0Open access