DATA ANALYSIS

MOTIVATION:

•12 Popular self hosted e-commerce Applications (deployed over 2M websites Which represent over 50% of entire ecommerce websites) 

• 22 critical ACIDRain attacks identified and Verified

An application is vulnerable if – Anomalies possible (Under concurrent API Access, the appli- cation may exhibit behaviors (i.E., anomalies) that could not have arisen Under a serial execution. )

 – Sensitive invariants. The anomalies arising From concurrent access lead to violations of Application invariants. 

SOLUTION:

• Execute API calls against a live application And database to generate a (possibly sequential) trace of database activity. 

• Analyze the trace for potential anomalies That could arise under concurrent Execution. 

PREVENTION:

– Select for update 

– User level concurrency control. • Prevent concurrent calls in the same session 

– Single read of data

 – Multiplevalidations. 

• They opened issues on Github • 18 different vulnerabilities reported • 7 of them are confirmed • One of them has a feedback “use your Brain! Its [sic] not hard to come up with a Solution that does not involve coding.

INTRODUCTION to Alpine Meadow:

**A new automated machine learning tool, Alpine Meadow, is developed

-- The tool is interactive, claimed to be an important aspect for fast operation -- The tool selects among the options the best possible setting for a given ML Task and constructs a final pipeline in an automated way 

* It is claimed to outperform many of its equivalents in a number of cases

AutoML APPROACH::

 AutoML or “Learning to Learn” is an approach in ML systems. The aim is to give the best prediction result in the fastest way. Based on automatically selecting the best possible; data processing steps-- learning algorithm-- hyperparameters.

THE NEED FOR IMPROVEMENTS IN AutoML:

Current AutoML systems Take days, weeks to complete. Usually do not allow user intervention. Providing a quick response is important. If computing powers are at the limit, user interaction might save time.

CONTRIBUTIONS OF ALPINE MEADOW:

The tool is interactive; showing the positive aspects of interactions. Pipeline selection provides new insights for AutoML approaches.

*THE SYSTEM OVERVIEW:

*The approach is started with mimicking a data scientist. *Optimize and automate all the step as much as possible.* Build many pipelines; select the most plausible one; show the results of the Best one.* Selection process is analogous to query optimization.

A solution for finding joinable tables proposed - A table and a join column given, the system Finds all the joinable tables with a cheaper Method: overlap set similarity search.

CONCLUSION:- JOSIE with its cost function is adaptive to new data Distributions - Suitable for large data lakes - Outperforms an approximate algorithm - As the future work - Estimation of set intersection size based on token Frequencies - Using past statistics, automated selection of query Columns - Fuzzy join