Impact of Temporary Tables and Table Variables on Query Performance in SQL Server 2012

2017 ◽  
Vol 7 (5) ◽  
pp. 23-26
Author(s):  
Mrityunjay Kumar Gaurav ◽  
Keyword(s):  
Sql Server ◽  
Query Performance

scholarly journals Novel Investigation Methodologies to Identify the SQL Server Query Performance

2015 ◽  
Vol 8 (27) ◽  
Author(s):  
Muthukumar Murugesan ◽  
K. Karthikeyan ◽  
K. Sivakumar
Keyword(s):  
Sql Server ◽  
Query Performance

scholarly journals Evaluation of Sub Query Performance in SQL Server

2014 ◽  
Vol 68 ◽  
pp. 00033 ◽  
Author(s):  
Tanty Oktavia ◽  
Surya Sujarwo
Keyword(s):  
Sql Server ◽  
Query Performance

SINH TỰ ĐỘNG TRIGGER TRÊN NGÔN NGỮ T-SQL HỖ TRỢ ANCHOR MODELING TRONG SQL SERVER

2017 ◽  
Author(s):  
Nguyễn Trần Quốc Vinh ◽  
Huỳnh Xuân Hiệp ◽  
Trần Đăng Hưng ◽  
Hoàng Ngọc Hiển ◽  
Nguyễn Văn Vương
Keyword(s):  
Sql Server

Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

2019 ◽  
Vol 64 (2) ◽  
pp. 53-71
Author(s):  
Botond Benedek ◽  
Ede László
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Naive Bayes ◽  
Insurance Industry ◽  
Naïve Bayes ◽  
Sql Server ◽  
Categorical Variables ◽  
Automobile Insurance ◽  
Price Determination ◽  
Mining Tool

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

ANALISA DAN PERANCANGAN SISTEM PARKIR PADA PT. KMK GLOBAL SPORTS

Jurnal Teknik ◽  
2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Irfan Nasrullah
Keyword(s):  
Sql Server

PT. KMK Global Sports merupakan salah satu perusahaan sepatu di Tangerang. Salah satu pelayanan perusahaan ini adalah masalah parkir kendaraan untuk karyawan dan tamu. Pelaksanaan sistem parkir pada PT. KMK Global Sports kurang maksimal karena belum adanya sistem terkomputerisasi untuk meningkatkan pelayanan yang baik. Metode perancangan yang digunakan adalah metode yang berorientasi objek melalui tahap pembuatan UML dan pembuatan database. Tujuan diadakan penelitian ini adalah untuk merancang sebuah sistem informasi parkir. Program yang digunakan dalam perancangan sistem ini menggunakan Power Builder sebagai bahasa pemrograman dan SQL server sebagai databasenya. Hasil penelitian menunjukan bahwa analisa dan perancangan sistem informasi parkir dapat memberikan informasi yang lebih cepat, tepat dan akurat. Dengan diperkenalkannya sistem ini maka diharapkan akan mempermudah dalam pelaksanaan sistem kerja yang sudah ada. Kata kunci : Parkir, Analisa, Perancangan, Sistem Informasi.

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