Integration of Artificial Intelligence and Database Management System: An Inventive Approach for Intelligent Databases

Nihalani, Neelu; Silakari, Sanjay; Motwani, Mahesh

doi:10.1109/cicsyn.2009.54

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…AI/DB integration is crucial not only for next generation computing but also for the continued development of DBMS technology. The motivations driving the integration of these two technologies include the need for (a) access to large amounts of shared data for knowledge processing, (b) efficient management of data as well as knowledge, and (c) intelligent processing of data (Nihalani et al 2009). One of the major types of integrations of AI and DBMS technologies in decision making support systems (DSSs) are intelligent databases.…”

Section: Integration Of Artificial Intelligent and Dbms Technologiesmentioning

confidence: 99%

Introduction to Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents definitions and descriptions of intelligent decision support systems (IDSSs) and analyzes the technology and AI methods, which serve as bases of the IDSS. Scholars have offered various definitions of IDSS. Every one of them accents that an intelligent decision support system is a DSS, which makes extensive use of artificial intelligence techniques. Artificial intelligence techniques can be utilized in all the components of IDSSs, such as in the data base, knowledge base, model base, user interface and the rest. Therefore this chapter deliberates the intelligent databases, hardware (sensors, iris camera hardware, hardware for fingerprint biometric identification, etc.) and computer human interfaces (gesture, intelligent user, motion tracking, voice and natural-language interfaces) in intelligent decision support systems.

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Section: Integration Of Artificial Intelligent and Dbms Technologiesmentioning

confidence: 99%

Introduction to Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…Furthermore, they also obtained an improved portion and reproduction method that can be functional at the early phase of the disseminated record when no in the sequence of the query implementation is accessible. Neelu Nihalani et al [7] (2009) expressed their justification for the usage of database management systems and AI systems, research, and the integration of these two technologies. The need for includes the need for a huge quantity of common information for information to be dealt out, well-organized administration of information as well as understanding, and smart dealing out of data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Data integration is an issue at the convergence of the areas of Server Applications and Intelligent Systems. The objective of a data integration framework is to give a standardized interface to a huge number of data sources, regardless of whether they are inside one endeavor or on the www [7][8]. The vital difficulties in data integration emerge because the data sources being incorporated have been planned freely for self-directed functions and their substance is connected to subtlety.…”

Section: Integration Of Db and Aimentioning

confidence: 99%

AI Based Learning Model Management Framework for Private Cloud Computing

Dhaya¹,

Dhaya²,

Kanthavel³

2022

Journal of Internet Technology

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<p>Artificial Intelligence (AI) systems are computational simulations that are “educated” using knowledge and individual expert participation to replicate a decision that a professional would make provided the same data. A model tries to simulate a specific decision loop that several scientists would take if they had access to all kinds of knowledge. To convey a model, you make a model asset in AI Platform Prediction, make a variant of that model and, at that point, interface the model form to the model record put away in Cloud Storage. AI and DB information sharing are essential for cutting-edge processing for DBMS innovation. The inspirations promoting their incorporation advances incorporate the requirement for admittance to a lot of data that is shared information handling, effective administration of data as information, and astute preparation of information. Notwithstanding these inspirations, the plan for a smart information base interface (IDI) was likewise spurred by the craving to save the considerable speculation spoke to by most existing data sets. A few general ways to deal with the connectivity of AI and databases and different improvements in the area of clever information bases were already examined and announced in this paper.</p> <p> </p>

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“…There are different types of database including relational and cloud databases and contain both present as well as historic data thereby providing efficient and fluent use of stored information for a variety of applications (Bhatia et al, 2013). They facilitate the storage, manipulation and retrieval of data under efficient information management systems (DBMS) resulting in effective task allocation and increase in productivity (Nihalani et al, 2009). The literature notes database systems are important in the management of data and with advancement in web technology, they have become repository of large amounts of data (Ma, 2007).…”

Section: Integration Of Ai and Database Into Thermal Analysis Techniquesmentioning

confidence: 99%

“…and artificial intelligence to store, process and analyse data (Parsaye et al, 1989;Bhatia et al, 2013). This represents an extension to the traditional databases, which are user interactive and apply artificial intelligence at three levels of database intelligence [high-level tools, user-interface and underlying database engine] (Nihalani et al, 2009). Since both artificial intelligence and database management systems represent well developed technologies, a case can be made for their integration.…”

Section: Integration Of Ai and Database Into Thermal Analysis Techniquesmentioning

confidence: 99%

The role of technological innovation in plastic production within a circular economy framework

Getor

Mishra

Ramudhin

2020

Resources, Conservation and Recycling

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The growing plastic waste problem has been highlighted by nature programmes like the British Broadcasting Corporation (BBC) War on Plastic series with the World Economic Forum (WEF) 2016 report estimating that by 2050, there could be more plastic than fish in the world's oceans. This has prompted the call for a shift from the linear model of tackling the plastic waste issue to that of a Circular Economy model with increasing demand for recycled resin to be incorporated into the plastic production process alongside virgin resin. However, recycled content are often associated with problems like lack of stability between various combinations of virgin/recycle polymers, which may lead to variations in temperature and inconsistency in the ease of flow attributable to the different temperature and pressure regimes under which virgin raw material and recycled feedstock melt and cool. In this paper, our main contribution is a proposed framework for integrating an enhanced AI/DB interface for analysing historical and real time information/data into a joint DSC-TGA system that allows for simultaneous quality control checks and thermal stability tests on different virgin-recycled resin mixing ratios. The database comes equipped with proven estimation methods, testing and statistical analysis results of different resin specimens to serve as a reference point for production engineers in arriving at desired check and test conditions. The information will be particularly useful to plastic product manufacturers when making decisions regarding the right virgin-recycled mix ratio conditions when using different batches of recycled resin or when they switch a supplier.

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Integration of Artificial Intelligence and Database Management System: An Inventive Approach for Intelligent Databases

Cited by 10 publications

References 18 publications

Introduction to Intelligent Decision Support Systems

Introduction to Intelligent Decision Support Systems

AI Based Learning Model Management Framework for Private Cloud Computing

The role of technological innovation in plastic production within a circular economy framework

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