What applications are recommended for SQL Server auditing and, more specifically, fraud investigations?
I need a tool that allows an end user to correlate data values to find fraud patterns. This tool must allow tuning as needed to reduce false positives.
It's also important that it be fairly intuitive. Ideally, once in place it would allow an end user unfamiliar with SQL to interface with it directly and customize using a GUI interface.
Suggestions?
It varies from simple business rules - user of type X aren't allowed to change discounts, no more than N uses of a coupon.
Through to some very clever Bayesian inference engine stuff that finds customer X's surname is the arabic translation of Mr Y's name who signed for him as a mortgage guarantee and they claim different home addresses but in the same zip code. This stuff gets very '6figure' pricey
Data-mining is used by law enforcement and credit card companies to stop criminals. There are patterns in large data sets that can reveal a greater motive. The more data the law enforcement has, the better they can track down the criminal(s).
You want to gather as much data as you can about a crime that may happen. This means you want to run a Network Intrusion Detection System (NIDS) on the Database's network. Snort is a very good NIDS and its free and open source. You wan to provide as much evidence of a crime to law enforcement and the FBI will LOVE your snort logs. I say when because its only a matter of time.
Related
I want to conduct an experiment with ten factors ( factors like costs and capacities) to know the influence of each factor on the optimum value of an optimization problem. I want to know the number of levels required for each factor and the number of experiments required with factor levels for each experiment.
Cost of experiment is not a matter because these are experiments are going to be run using a software, but the time required to run is important because if large number of experiments are required the time will be more.
please throw light.
You have Minitab as a tag on this question; that said, Minitab has an excellent capability to help in planning DOEs. Go to the Assistant menu, then DOE, then Plan and Create...
If you click "Create Modeling Design" in the optimization experiment path, it will give you the setup screen where you can specify response, optimization objective, factors, etc. Notice that the design is a typical factorial-type design where low/high values are used in each experimental run. This should give good results, but just to let you know there are other design types that can be even better given the circumstances of each situation. For instance, you mentioned these are software experiments -- there is a nice design called a "Space Filling Design" which creates factor design points (not necessarily at low/high values) to optimally fill the design search space. These designs are often used for computer simulation experiments.
An excellent text on DOE is https://www.amazon.com/Design-Analysis-Experiments-Douglas-Montgomery/dp/1118146921
I have this scenario:
You have a factory process line which runs 24/7. Downtime is extremely expensive.
The software controlling all different parts must use a shared form of database storage
The main reason for this is to know in which state the factory is in. For example some products can be mixed when using the same set of equipement and others DEFINITELY not.
requirements:
I want to the software be able to detect that an error in one part of
the plant must result in some machine shutdown more then 1 km away. so stoing data in the plc's is not an option.
Updates and upgrades to the factory environment are frequent
load (in computer terms) will be really low.
The systems handles a few hunderd assignments a day for which calculations / checks are done followed by instructions send for the factory machines. Systems will be bored most of the time. Most important requirement is the central computer system must be correct and always working.
I was thinking to use a dynamo based database (riak or cassandra) where data gets written to multiple machines with each machine having the whole database
When one system goes down it will go down unoticed. A Traditional sql databse might be more of a pain to upgrade when tables changes and this master slave is harder to configure.
What would be your solution?
Network has been made redundant and most other single points of failure to. The database system is critical because downtime of the db means downtime for the entire plant not just one of the machines which is acceptable.
How to solve shared state problem.
complexity in the database will not be a problem. I will be more like a simple key value store to get the most current and correct data.
I don't think this is a sql/nosql question. All of Postgres, MySQL and MS SQL Server have some kind of cluster or hot standby option.
Configuration is a one-time thing, but any NoSQL option is going to give you headaches from top-to-bottom of code, if you are trying to do something fundamentally relational on a platform that has given up relational for the purposes of running things like Amazon or Facebook. The configuration is once, the coding is forever.
So I would say stick with a tried and true solution and get that hot replication going.
This also provides a solution for upgrades. The typical sequence is to "fail over" to the standby, upgrade the master, flip back to the master, upgrade the standby, and resume. With details specific to the situation of course.
Use an established RDBMS that supports such things natively
Do you really want to run a 24/7 mission critical system on something that may be consistent at any point in time?
You need to avoid single points of failure.
All the major players in our dbms world offer at least one way to avoid making the database itself a single point of failure. I might question whether they can propagate changes fast enough for your manufacturing processes. (Or is data update not really an issue? Can't really tell from your question.) My db work in manufacturing is limited to the car and the chemical industry. Microseconds didn't matter to them.
But the dbms isn't the only thing that can fail. "Always working" means that the clients have to always be working, too. Client hardware, connections to the network, the network and network servers themselves all probably have single points of failure. Failure-tolerant servers have multiple power supplies, multiple NICs, etc.
"Always working" is really expensive. I have a feeling that the database isn't going to be the biggest problem for your company.
Hey.. i wanna know which time is a good accesstime, because i'm searching for a good sql database and hsqldb says their accesstime is 12ms... <-- good?
I think it would depend on your needs. Is it for a web server or a desktop application? The amount of data is also important, because reading lots of small records will perform differently than reading a few large records. Access time is also based upon your hardware, software and maybe even some other factors.
For example, you can use a database with lightning-fast access, but if your users need to connect to it over a 5 megabit VPN connection, passing through three different proxies and with trafic world-wide, your database would then just be a waste of power.
Basically, it's a marketing thing that they're claiming. It's a good product but don't just focus on access time. Make sure you also look at your other needs. Another system might just perform better, even if it has a slower acess time, because it is more optimized in reading it's indices and stuff.
So, what do you want, exactly?
I don't think access time tells you anything, really. If you have slow or incorrectly configured storage, then this access time metric will be dwarfed by how much time is spent on waits and split I/Os. Network latency is also a factor, since I'm guessing you probably won't want to have your code on the same machine as your database, and you will most likely have a few network devices you'll need to traverse in your production environment.
In my experience, all the database platforms these days will all perform adequately if configured correctly and paired with a complementary application. Pick the DBMS that best fits your requirements, follow the best practices for configuration of the DBMS on your hardware, and you should be please with the outcome.
I'm working on the technical architecture for a content solution integration. The data from the solution provider runs to millions of rows and normalised to 3NF. It is updated on a regular schedule (daily most likely) and its data is split down to a very granular level of atomicity.
I need to search and query this data and my current inclination is to leave the normalised data alone and create a denormalised database from its data (OLAP to OLTP). The 'transfer' can be a custom built program that can contain the necessary business logic in addition to the raw copying power and be run at a set schedule as required. The denormalised database would then reduce the atomicity and allow the keyword searches and queries to run efficiently. I was looking at using Lucene .NET for the keyword work on the denormalised database.
So before I sing loudly from the hills that this is the way forward, I wanted some expert opinion on this and what is the perceived "best practise". Is the method I have suggested the best way forward considering the data I will be provided? It was suggested that perhaps I could use a 'search engine' to search the normalised data. This scared the hell out of me, but raised the question; what search engine and how?
Opinions, flames, bad language and help appreciated :)
I have built reporting databases and data warehouses based on data stored in normalized form. There is quite a bit of work involved in the transfer program (ETL). Given your description of the data feed, maybe some of that work has been done for you by the feeder.
Millions of rows isn't a lot, these days. You may be able to get away with report oriented views into the existing database. Try it and see.
The biggest benefit to building an OLAP oriented database is not speed. It's flexibility. "We love this report, but now we want to see it weekly and quarterly instead of monthly. Bam! Done!" "Can you break it down by marketing category instead of manufacturing category? Bam! Done!" And so on.
A resonably normalized model (3NF/BCNF) provides the best average performance and the least amount of modification anomalies for the largest number of scenarios. That's big, so I would start from there. As your requirements are fuzzy, it's seems like the most sensible option.
Actually, the most sensible thing would be to go over the requirements until they are a bit more "crisp" ;)
Also, if you could get your hands on a few early extracts from your data provider you could experiment with it and get a feeling for the data distributions (not all people live in one country, and some countries holds more people than others. Not all people have children, and the number children per person is vastly different depending on the country). This is a major point and it is crucial that the optimizer can make good decisions.
Other than that, I agree with everything Walter said and also gave him my vote.
I keep hearing from associates about grid computing which, from what I can gather, is highly distributed stuff along the lines of SETI#Home.
Is anyone working on these sort of systems for business use? My interest is in figuring out if there's a commercial reason for starting software development in this field.
Rendering Farms such as Pixar
Model Evaluation e.g. weather, financials, military
Architectural Engineering e.g. earthquakes.
To list a few.
Grid computing is really only needed if you have a lot of WORK that needs to be done, like folding proteins, otherwise a simple server farm will likely be plenty.
Obviously Google are major users of Grid Computing; all their search service relies on it, and many others.
Engines such as BigTable are based on using lots of nodes for storage and computation. These are commercially very useful because they're a good alternative to a small number of big servers, providing better redundancy and cost effective scaling.
The downside is that the software is fiendishly difficult to write, but Google seem to manage that one ok :)
So anything which requires big storage and/or lots of computation.
I used to work for these guys. Grid computing is used all over. Anyone who makes computer chips uses them to test designs before getting physical silicon cut. Financial websites use grids to calculate if you qualify for that loan. These days they are starting to replace big iron in a lot of places, as they tend to be cheaper to maintain over the long term.