Power data extractor how to#

Events configuration options allows to be more specific on what information will be captured during the SQL audit process, for this demo, inside the Global Fields we’ve chosen:.

Staying on the Events tab, highlight the sql_statement_completed from the Selected events grid and choose Configure:

Highlight it in the grid and choose right arrow button “>” to move the event to the Selected events box:.

Navigate to Events tab and into Events library search for sp_statement_completed.

In the Events tab we will configure the policy that will tell SQL Server what type of auditing data we are collecting, and to track SELECT statements:.

In the New Session wizard, inside the General tab give your session a familiar name: In the Object Explorer, expand the Management node > Extended Events > Right click on the Sessions node and choose New Session: Let us quickly jump into creating a session file to track access, more particularly SELECT statements against the database using SQL Server Management Studio (SSMS) user interface. In this article, we’ll be using Extended Events sessions for demonstration purposes. There are fewer options to collect, store, and organize auditing data using the native SQL auditing techniques.

Power data extractor how to#

In this article, we will explore the possibilities of how to collect audit data and put it to use in the context of data analytics using MS Power BI, create valuable information, and learn about databases activities and data access.Įxtracting data using native auditing techniques To an extent, SQL auditing data is not an exception, and it might play an important role while screening for potential data breaches, learning about activity trends and data flows.

Therefore, many companies are challenged with keeping a close eye on data governance processes that expand together with the amount of data being accumulated. Our finding includes that the statistical predictors generated from apparent power can improve the accuracy of the significantly-imbalanced smart home data classification.Growing datasets and information in the modern digital age become considerable businesses’ assets as data drives many important decisions for organizations. Our finding is that the simple statistical predictors that reveal smart home occupants appliance usage patterns and energy consumption details can be obtained through smart home data analytics. We constructed five machine learning models using these predictors and evaluated them using random forest classification and the qualitative measures – classification accuracy, out-of-bag error, and misclassification error. We generated the predictors using block-based statistical information of the real power and apparent power associated with each appliance class type. We used an open data set of Smart* project and its real power and apparent power variability to accomplish this goal. The goal of this paper is to perform an experimental research and show that simple statistical predictors can reveal usage patterns of the electrical appliances from smart meter and sensor readings.