Data access and processing system

1. A distributed data analysis system that analyzes Limit Order Book (LOB) data, said system including:

a processor system having at least one processor and a memory system;

a virtual machine interface module that allocates, by the processor system, cloud resources for at least one virtual machine, the virtual machine interface module creates, by the processor system, the at least one virtual machine, wherein the at least one virtual machine is configured to allocate cloud resources for at least one compute unit, wherein the at least one virtual machine creates, by the system, the at least one compute unit, and the at least one computer unit processes the LOB data, the at least one compute unit and the LOB data being collocated in the system;

an update module that updates, by the system, LOB data from at least one electronic trading venue;

a monitoring module that tracks, by the system, activity of at least one user on the system, and;

a software library having at least one algorithm for processing and/or analyzing the LOB data;

wherein the system is implemented in a cloud computing environment;

wherein computing resources are allocated to at least one user according to the requirements of the at least one user; and

wherein an instance of LOB analysis algorithm runs collocated in the cloud with the LOB data, the LOB analysis algorithm preforms a regression analysis, filters LOB data, or searches for at least patterns or correlations.

2. The system according to claim 1 , wherein: the LOB data are processed to have a common format.

3. The system according to claim 1 , wherein: each of at least one user of the system comprises at least one sub-user.

4. The system according to claim 1 , wherein: each of at least one sub-user is allocated a sub-user-specific file area on a user-specific filesystem.

5. The system according to claim 4 , wherein: each user-specific filesystem is sized dynamically.

6. The system according to claim 4 , wherein: each sub-user-specific file area is sized dynamically.

7. The system according to claim 6 , wherein: each sub-user specific file area is mapped to a computer belonging to the sub-user.

8. The system according to claim 1 , wherein: the at least one compute unit is a virtual machine unit.

9. The system according to claim 1 , wherein: the system further includes an update module for online or batch updating the LOB data.

10. The system according to claim 1 , wherein: the system further includes a control module that provides and option to control which LOB data each user may access and/or process.

11. The system according to claim 1 , wherein: the system further includes a download control module that provides an administrative-option to control which data each user may download from the system.

12. The system according to claim 1 , wherein: the LOB data is assembled from a plurality of electronic trading venues.

13. The system according to claim 1 , wherein: the system further includes a connection module for allowing a plurality of users to connect to the system.

14. The system according to claim 1 , wherein: the monitoring module further implements a logging method to log at least one sub-user's activity on the system.

15. The system according to claim 1 , wherein: the monitoring module further implements a billing method to generate billing data for at least one user.

16. The system according to claim 1 , wherein: the monitoring module further implements an audit method to audit at least one user.

17. The system according to claim 1 , wherein: the at least one algorithm is a pattern recognition algorithm that is deployed in the cloud having different instances with different locations, the locations of the instances being based on locations of the LOB data being searched for patterns.

18. The system according to claim 1 , wherein:

the monitoring module utilizes user data based on the resources used by the user and/or data accessed by the user.

19. The system according to claim 1 , wherein: the cloud computing environment is a public cloud computing environment.

20. The system according to claim 1 , wherein: the control module is downloadable and allows control over which data each user has permission to download from the system.

21. The system according to claim 20 , wherein: the LOB analysis algorithm searches for the patterns via an instance of a pattern recognition algorithm running on the same rack in the cloud with the LOB data being searched for patterns.

22. A distributed data analysis method, for use in analyzing Limit Order Book (LOB) data, comprising:

allocating, cloud resources for at least one virtual machine, via a virtual machine interface of a system, the system having at least a machine having at least one processor and memory, creating, by the at least one virtual machine interface at least one virtual machine, allocating, via the at least one virtual machine, cloud resources for at least one compute unit, creating via the at least one virtual machine at least one compute unit, wherein the at least one compute unit and the LOB data are collocated, and

processing, via the at least one compute unit the LOB data;

updating, via an update module of the system, the LOB data from at least one electronic trading venue, and;

monitoring, via a monitoring module of the system, activity of at least one user on the system, and;

the memory storing a software library having at least one algorithm, which when implemented, the at least one algorithm processes and/or analyzes the LOB data;

implementing the system in a cloud computing environment; and

allocating computing resources to at least one user according to the requirements of the at least one user;

wherein an instance of LOB analysis algorithm runs collocated in the cloud with the LOB data, the LOB analysis algorithm at least preforms a regression analysis, filters LOB data, or searches for at least patterns or correlations.

23. A distributed data analysis system configured to analyze Limit Order Book (LOB) data, said system comprising:

one or more machines, each machine including at least one or more processors; and

memory storing one or more machine instructions, which when implemented, cause the one or more machines to implement, via the one or more processors, a method including at least

allocating, cloud resources for at least one virtual machine, via a virtual machine interface of the system, at least one virtual machine, creating, via the at least one virtual machine, at least one compute unit, wherein the at least one compute unit and the LOB data are collocated within the system, and

processing, via the at least one compute unit, the LOB data;

updating, via an update module of the system, the LOB data from at least one electronic trading venue, and;

tracking, via a monitoring module of the system, activity of at least one user on the system, and;

the memory storing a software library having at least one algorithm, which when implemented, the at least one algorithm processes and/or analyzes the LOB data;

allocating computing resources to the at least one user according to the requirements of the at least one user; and

implementing the system in a cloud computing environment;

wherein an instance of LOB analysis algorithm runs collocated in the cloud with the LOB data, the at least one algorithm that processes and/or analyzes the LOB data at least preforms a regression analysis, filters LOB data, or searches for at least patterns or correlations.

24. The system according to claim 23 , wherein: the at least one algorithm is a pattern recognition algorithm, wherein when the at least one algorithm is implemented, the at least one algorithm searches patterns within one LOB and patterns found based on more than one LOB.

25. The system according to claim 23 , the LOB data being historical data.

26. The system according to claim 23 , the method further including simulating market behavior based on message passing associated with LOB data.

27. The system according to claim 23 , the method further including simulating latencies of messages passing between LOBs.