Database Performance considerations while Architecting

Performance considerations while architecting database access

Turning on features that application only.
N-Tier strategy gives maximum performance as opposed to a direct client-to-database connection.
Use Optimal Managed Provider such as System.Data.SqlClient to gain performance.
Stored Procedures usage where ever possible.
Avoid Auto-Generated commands of data adapter. Gives better performance in critical applications.
Prefer Data Reader, when you dont need to cache your data. using data reader can provide you with an enormous performance boost.
Data Reader's CommandBehavior.Sequential Access can be used often as possible. If you dont need to work the whole object at once, will give you much better performance.
Keep your Datasets lean.
Particularly when designing for disconnected approach, make several connections in sequence rather than holding a single connection open for a long time.

Comments

ABOD and its PyOD python module

Angle based detection By Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek Ludwig-Maximilians-Universität München Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By Yue Zhao Zain Nasrullah Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Zheng Li jk Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing. Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based similarity measures for text data. Object o is an out

TableSense: Spreadsheet Table Detection with Convolutional Neural Networks

- By Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, Dongmei Zhang Microsoft Research, Beijing 100080, China. Beihang University, Beijing 100191, China Paper Link Abstract Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for tab

DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY

-By Raghavendra Chalapathy University of Sydney, Capital Markets Co-operative Research Centre (CMCRC) Sanjay Chawla Qatar Computing Research Institute (QCRI), HBKU Paper Link Anomaly detection also known as outlier detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions Hawkins defines an outlier as an observation that deviates so significantly from other observations as to arouse suspicion that it was generated by a different mechanism. Aim of this paper is two-fold, First is a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore the adoption of these methods

SRI Blog

Search This Blog