165 research outputs found
Visual modeling of behavioural properties in the LVM for XML using XSemantic nets
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)
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
Π Π΅ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ Π±Π°Π· Π΄Π°Π½Π½ΡΡ ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ Π°ΡΠ΄ΠΈΡΠ°
ΠΠ΄Π½ΠΈΠΌ ΡΠ· Π³ΠΎΠ»ΠΎΠ²Π½ΠΈΡ
Π΄ΠΆΠ΅ΡΠ΅Π» Π·Π°Π³ΡΠΎΠ· Π±Π΅Π·ΠΏΠ΅ΡΡ Π² ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΠΉΠ½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Π²Π·Π°Π³Π°Π»Ρ, Ρ Π±Π°Π·Π°Ρ
Π΄Π°Π½ΠΈΡ
Π·ΠΎΠΊΡΠ΅ΠΌΠ°, Ρ ΡΠ½ΡΠ°ΠΉΠ΄Π΅ΡΠΈ [1]. ΠΡΠΈΡΠΈΠ½Π°ΠΌΠΈ ΡΡΠ°Π·Π»ΠΈΠ²ΠΎΡΡΡ Π±Π°Π· Π΄Π°Π½ΠΈΡ
(ΠΠ) Π΄ΠΎ ΡΠ½ΡΠ°ΠΉΠ΄Π΅ΡΡΡΠΊΠΈΡ
Π°ΡΠ°ΠΊ Ρ ΠΏΠΎΠΌΠΈΠ»ΠΊΠΈ ΡΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΠΏΠΎΠ»ΡΡΠΈΠΊΠΈ Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ ΠΠ. Π§Π°ΡΡΠΎ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Π½Ρ ΠΏΠΎΠ»ΡΡΠΈΠΊΠΈ Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ ΡΠΎΡΠΌΡΠ»ΡΡΡΡΡΡ ΡΠΊ ΡΠ΅Π°ΠΊΡΡΡ Π½Π° ΠΏΠΎΡΠΎΡΠ½Ρ ΠΏΠΎΡΡΠ΅Π±ΠΈ, ΡΠΊΡ Π· ΡΠ°ΡΠΎΠΌ ΠΏΠ΅ΡΠ΅ΡΡΠ°ΡΡΡ Π±ΡΡΠΈ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΈΠΌΠΈ, Π° ΡΠΎ Ρ Π·ΠΎΠ²ΡΡΠΌ, Π·Π°ΠΉΠ²ΠΈΠΌΠΈ. Π£ ΡΡΠ°ΡΡΡ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΡΡΡΡ ΠΏΡΠ΄Ρ
ΡΠ΄ Π΄ΠΎ ΠΊΠΎΡΠ΅ΠΊΡΡΠ²Π°Π½Π½Ρ ΠΏΠΎΠ»ΡΡΠΈΠΊΠΈ Π±Π΅Π·ΠΏΠ΅ΠΊΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΏΡΠΎΡΡΠ»ΡΠ² ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ².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
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ΠΡΠ΅Π½ΠΊΠ° ΠΈ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΡΠΈΡΡΠ΅ΠΌ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π²Π΅Π±-ΡΠ΅ΡΠ²ΠΈΡΠΎΠ²
Survey: Models and Prototypes of Schema Matching
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
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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