Checkpoint based multi-version concurrency control mechanism for remote healthcare system

Ghosh, Ammlan; Chaki, Rituparna; Chaki, Nabendu

doi:10.1109/icacci.2016.7732076

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Conversational agents or commonly known as chatbots are getting much attention in many industries. For instance, the research conducted by [5] designed a healthcare system to provide assistance in the remote area to overcome the absenteeism of medical support, whereas [6] have advanced the medical industry by scheduling intelligent appointment. The author in [7] attempted to offer medical support via chatbot to predict the disease based on the symptoms given by the user besides providing the proper cure or treatment.…”

Section: Related Workmentioning

confidence: 99%

“…This is vital as not all the relevant information is portrayed under the corporate FAQs; this approach could assist client to speed up the information retrieval by having answer lookup from the corporate portal. Initially, the content from the website will be crawled via crawler to perform question and context encoder as in (4) and (5) in which r t Q refers to the new representation of all words in the questions, BiRNN Q refers to Bi-directional Recurrent Neural Network,…”

Section: Fig 4 Conversation Via Retrieval-based Modelmentioning

confidence: 99%

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An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

Leong*¹,

Goh²,

Kumar³

2019

IJITEE

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In accordance to the Fourth Industrial Revolution, the emergence of the digital transformation alongside with the collaboration between networked machines and human beings in decision-making has attracted much attention globally. The utilization of conversational agent to convey information on behalf of human have gained popularity and been used extensively. As a result, this research intends to examine and to innovate the current corporate website by providing the capabilities to deliver instantaneously replies and reliable information through the integration of a conversational agent via deep learning, comparable to communicating with the competent customer service consultant. This study is reliant on the Artificial Intelligence (AI) to offer natural language processing (NLP) by presenting retrieval-based model and Deep Learning to enable the conversation agent to make smarter and better decision in generating reliable and up-to-date responses. Furthermore, this research intends to automate the process of minimizing the need for human interference, specifically the botmaster to perform the knowledge maintenance manually.

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Section: Related Workmentioning

confidence: 99%

Section: Fig 4 Conversation Via Retrieval-based Modelmentioning

confidence: 99%

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

Leong*¹,

Goh²,

Kumar³

2019

IJITEE

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“…The application of artificial conversational agents to communicate information on behalf of humans has been a major trend that is being utilized intensively, especially in the field of commerce [1], [2], [3], [4], [5], entertainment [6], tourism [7], medical [8], [9], [10], [11], [12], [13] as well as education [14], [15], [16], [17], [18]. According to the author in [19], by implementing AI, the banking industry could enjoy possible cost savings of up to 25 percent in Information Technology operations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Conversational Agent via Context Question Answering Model

2020

IJATCSE

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The usage of a conversational device communicate knowledge on behalf of humans has attracted popularity and has been widely employed. As a consequence, this study seeks to analyze and improve the current business platform by providing the ability to offer immediate feedback by integrating Deep Learning to the conversational agent. Our research focuses on Artificial Intelligence (AI) to include Natural Language Processing (NLP) by offering a paradigm focusing on Deep Learning via Context Question Answering Model. This model enables the conversational agent to make wiser and stronger choices to be able to understand and generate their own answers to unknown queries. The testing will be performed in the quantitative approach using Black-box Testing and User Experience (UX) Testing. The Black-box Testing findings show that the average time taken for Deep Learning to produce a response is approximately two seconds. As far as accuracy is concerned, the conversational agent is capable of attaining the accuracy of 67.65 percent. The accuracy could be improved from time to time by providing more training to the model. Apart from this, the test results from User Experience Testing indicates that 84.3 percent of the respondents are satisfied with the conversational agent, whereas 86 percent of respondents would use this conversational agent again. The experiments provide positive outcomes and evidence of the progress of this research, with tremendous contributions to the area of artificial conversational agent.

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“…The mechanism can meet the high degree of concurrency and low concurrency in the operating environment to a certain extent. The use of timestamp-based concurrency control in the case of low concurrency can shorten the transaction run time to a certain extent, improving existing efficiency; and in the case of high degree of concurrent use of multi-granularity lock mechanism can improve the original degree of coherence and improve efficiency [10].…”

mentioning

confidence: 99%

Research and Improvement of SQLite's Concurrency Control Mechanism

Zhao¹,

Ding²,

Ma³

et al. 2017

Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 20

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Abstract:On the basis of the study of SQLite and other concurrency control mechanisms in memory database, an adaptive concurrency control mechanism is proposed according to the operating environment of SQLite in intelligent mobile devices. The function and concrete flow of improved concurrency control are introduced and discussed. The data structure and the main functions of each part are analyzed. The function and performance of the improved concurrency control mechanism are tested. The results verify the correctness and validity of the adaptive concurrency control. IntroductionIn recent years, concurrency control, as one of the performance indicators of memory, has also gained rapid development, from the traditional lock-based pessimistic control mechanism to the current multi-granularity model, from the original optimistic concurrency control mechanism to a variety of improved optimistic concurrency control Mechanism, which all contributes to the upgrade of the memory database concurrency and efficiency. But SQLite is progressing slowly in concurrency control, still using traditional locking-based concurrency protocols, database-level locking granularity becomes a shackle of concurrency and results in lower degrees of concurrency. Considering the shortcomings of the original concurrency control mechanism and the characteristics of its operating environment, combined with the advantages of other concurrency control mechanisms, the original mechanism can be improved and a new concurrency control mechanism can be designed to improve the concurrency and efficiency of SQLite database[1,2]. 2.SQLite Concurrency Control 2.1 Lock Mechanism of SQLite. SQLite uses extensive database granularity locking, using a harsh two-stage blocking protocol. If a transaction applies for a lock, the lock is released only when the transaction completes, and the transaction cannot perform any conflicts with the lock until the lock is released. At the same time, all the blocking requests in each transaction precede all unlock requests, the two phases refer to the lock growth phase and the lock shrink phase, 1) the lock growth phase: before any data is read and written, the transaction first obtains the data of the blockade; 2) lock shrink phase: after the release of a blockade, the transaction no longer get any lock. The SQLite system manages a lock table that enables the transaction to be locked at the last minute to ensure maximum concurrency and efficiency [3,4].SQLite contains 5 different lock states: unlocked state, shared state, hold status, pending state, and exclusive status, each transaction can only be in one of the states at the same time. In addition to the unlocked state, each state has a lock corresponding to it: 1) shared locks; 2) reserved locks; 3) pending locks; 4) exclusive locks,. A file can only have an exclusive lock, any other lock and exclusive lock cannot coexist.2.2 Analysis of Lock Mechanism. At present, SQLite is widely used in intelligent mobile devices, in such an environment, the database concurrency is l...

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Checkpoint based multi-version concurrency control mechanism for remote healthcare system

Cited by 4 publications

References 9 publications

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

Deep Learning for Conversational Agent via Context Question Answering Model

Research and Improvement of SQLite's Concurrency Control Mechanism

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