165 research outputs found

    Visual modeling of behavioural properties in the LVM for XML using XSemantic nets

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    Due to the increasing dependency on self-describing, schema-based, semi-structured data (e.g. XML), there exists a need to model, design and manipulate semi-structured data and the associated semantics at a higher level of abstraction than at the instance or document level. In this paper, we extend our research and propose to visually model (at the conceptual level) and transform dynamic properties of views in the Layered View Model (LVM) using the eXtensible Semantic (XSemantic) net notation. First, we present the modeling notation and then discuss the declarative transformation to map the dynamic XML view properties to XML query expressions, namely XQuery

    Analysis of IEEE 802.11 (Wi-Fi)

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    Wireless Fidelity (Wi-Fi) is a technology that allows electronic devices to connect to a wireless LAN (WLAN) network, mainly using the 2.4 gigahertz (12 cm) UHF and 5 gigahertz (6 cm) SHF ISM radio bands. A WLAN is usually password protected, but may be open, which allows any device within its range to access the resources of the WLAN network. Devices which can use Wi-Fi technology include personal computers, video-game consoles, smartphones, digital cameras, tablet computers, digital audio players and modern printers. Wi-Fi is less secure than wired connections, such as Ethernet, precisely because an intruder does not need a physical connection. It follows the four layer TCP architecture and it has made remote locations more accessible and dropped costs

    РСорганизация ΠΏΠΎΠ»ΠΈΡ‚ΠΈΠΊΠΈ бСзопасности Π±Π°Π· Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΠΎ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌ Π°ΡƒΠ΄ΠΈΡ‚Π°

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    Одним Ρ–Π· Π³ΠΎΠ»ΠΎΠ²Π½ΠΈΡ… Π΄ΠΆΠ΅Ρ€Π΅Π» Π·Π°Π³Ρ€ΠΎΠ· Π±Π΅Π·ΠΏΠ΅Ρ†Ρ– Π² Ρ–Π½Ρ„ΠΎΡ€ΠΌΠ°Ρ†Ρ–ΠΉΠ½ΠΈΡ… систСмах Π²Π·Π°Π³Π°Π»Ρ–, Ρ– Π±Π°Π·Π°Ρ… Π΄Π°Π½ΠΈΡ… Π·ΠΎΠΊΡ€Π΅ΠΌΠ°, Ρ” інсайдСри [1]. ΠŸΡ€ΠΈΡ‡ΠΈΠ½Π°ΠΌΠΈ уразливості Π±Π°Π· Π΄Π°Π½ΠΈΡ… (Π‘Π”) Π΄ΠΎ Ρ–Π½ΡΠ°ΠΉΠ΄Π΅Ρ€ΡΡŒΠΊΠΈΡ… Π°Ρ‚Π°ΠΊ Ρ” ΠΏΠΎΠΌΠΈΠ»ΠΊΠΈ формування ΠΏΠΎΠ»Ρ–Ρ‚ΠΈΠΊΠΈ Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ Π‘Π”. Часто полоТСння ΠΏΠΎΠ»Ρ–Ρ‚ΠΈΠΊΠΈ Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ Ρ„ΠΎΡ€ΠΌΡƒΠ»ΡŽΡŽΡ‚ΡŒΡΡ як рСакція Π½Π° ΠΏΠΎΡ‚ΠΎΡ‡Π½Ρ– ΠΏΠΎΡ‚Ρ€Π΅Π±ΠΈ, які Π· часом ΠΏΠ΅Ρ€Π΅ΡΡ‚Π°ΡŽΡ‚ΡŒ Π±ΡƒΡ‚ΠΈ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΈΠΌΠΈ, Π° Ρ‚ΠΎ Ρ– зовсім, Π·Π°ΠΉΠ²ΠΈΠΌΠΈ. Π£ статті ΠΏΡ€ΠΎΠΏΠΎΠ½ΡƒΡ”Ρ‚ΡŒΡΡ ΠΏΡ–Π΄Ρ…Ρ–Π΄ Π΄ΠΎ корСктування ΠΏΠΎΠ»Ρ–Ρ‚ΠΈΠΊΠΈ Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ Π½Π° основі ΠΏΡ€ΠΎΡ„Ρ–Π»Ρ–Π² користувачів.One of the main sources of security threads in information systems in common and databases as a part, is insiders [1]. Mistakes connected with security policy development are the main purposes of database vulnarabilities related to the insiders' attacs. In most cases security policy is based on information that becomes useless with the time. In this article a new method for correction of security policy based on users' profiles was proposed

    Survey: Models and Prototypes of Schema Matching

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    Schema matching is critical problem within many applications to integration of data/information, to achieve interoperability, and other cases caused by schematic heterogeneity. Schema matching evolved from manual way on a specific domain, leading to a new models and methods that are semi-automatic and more general, so it is able to effectively direct the user within generate a mapping among elements of two the schema or ontologies better. This paper is a summary of literature review on models and prototypes on schema matching within the last 25 years to describe the progress of and research chalenge and opportunities on a new models, methods, and/or prototypes

    Encoding and Decoding Techniques for Distributed Data Storage Systems

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    Dimensionality reduction is the conversion of high-dimensional data into a meaningful representation of reduced data. Preferably, the reduced representation has a dimensionality that corresponds to the essential dimensionality of the data. The essential dimensionality of data is the minimum number of parameters needed to account for the observed properties of the data [4]. Dimensionality reduction is important in many domains, since it facilitates classification, visualization, and compression of high-dimensional data, by helpful the curse of dimensionality and other undesired properties of high-dimensional spaces [5]. Dimension reduction can be beneficial not only for reasons of computational efficiency but also because it can improve the accuracy of the analysis. In this research area, it significantly reduces the storage spaces
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