U.S. patent application number 13/719949 was filed with the patent office on 2014-01-02 for method and system for aggregating and managing data from disparate sources in consolidated storage.
This patent application is currently assigned to FTEN Inc.. The applicant listed for this patent is FTEN, Inc.. Invention is credited to Valerie BANNERT-THURNER, Brian CROWLEY, Jason TIMMES.
Application Number | 20140006244 13/719949 |
Document ID | / |
Family ID | 49779148 |
Filed Date | 2014-01-02 |
United States Patent
Application |
20140006244 |
Kind Code |
A1 |
CROWLEY; Brian ; et
al. |
January 2, 2014 |
Method and System for Aggregating and Managing Data from Disparate
Sources in Consolidated Storage
Abstract
Non-limiting example embodiments include methods and systems for
acquiring private financial data from multiple disparate sources.
The private financial data is normalized, aggregated, preferably
enhanced, and stored in secure storage. Entitled entities may
retrieve selected private financial data from that secure storage
efficiently, flexibility, and rapidly. Examples of financial
private data include non-liquidity destination related sources of
private data as well as liquidity destination related sources. A
non-limiting example of a computer-implemented, consolidated,
private financial data service is based on a secure,
permission-based, aggregated and consolidated data cloud, which
enables provision/distribution to one or more authorized parties
with legitimate interests selected portions of the consolidated,
private financial data.
Inventors: |
CROWLEY; Brian; (Oyster Bay,
NY) ; BANNERT-THURNER; Valerie; (Winchester, GB)
; TIMMES; Jason; (Wheat Ridge, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
FTEN, Inc. |
New York |
NY |
US |
|
|
Assignee: |
FTEN Inc.
New York
NY
|
Family ID: |
49779148 |
Appl. No.: |
13/719949 |
Filed: |
December 19, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61577188 |
Dec 19, 2011 |
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Current U.S.
Class: |
705/37 |
Current CPC
Class: |
G06Q 40/04 20130101 |
Class at
Publication: |
705/37 |
International
Class: |
G06Q 40/04 20060101
G06Q040/04 |
Claims
1. A method implemented in an information processing system having
multiple processors, the method comprising: receiving from multiple
data sources at the information processing system private financial
data related to financial articles of trade that are tradable on
one or more financial markets, where private financial data is
exclusively accessible by only a set of entitled entities
associated with the private financial data or transactions
involving the private financial data, and where the received
private financial data from the multiple data sources includes
multiple different data formats; normalizing, via the information
processing system, the private financial data in multiple different
data formats received from the multiple data sources into a
normalized data format and storing the normalized private financial
data in storage associated with the information processing system;
processing, via the information processing system, the normalized
private financial data with public financial data to provide
enhanced private financial data; storing the enhanced private
financial data; and distributing, via the information processing
system, selected portions of the enhanced private financial data to
entitled entities with legitimate rights to the selected portions
of the enhanced private financial data.
2. The method in claim 1, wherein the private financial data
includes one or more of: transactional data, margin requirements or
calls, trading position information, ownership information, or a
status of third parties as guarantors of, or providers of credit
for, specified transactions.
3. The method in claim 1, wherein the public financial data
includes one or more of: trading market data, trading reference
data, an identity of regulators with a legal right to private
financial data as authorized by owners of the private financial
data, or guarantors or creditors of transactions giving rise to
private financial data.
4. The method in claim 1, wherein the multiple data sources include
multiple public trading exchanges and one or more dark pools, and
wherein the private financial data is provided in drop copy files
associated with the multiple public trading exchanges and the one
or more dark pools.
5. The method in claim 1, wherein the processing step further
includes processing the normalized private financial data with
other, more static private financial data that includes one or more
of: custom haircuts, allocation relationships, mappings between
clearing and trading accounts, overnight positions, posted
collateral, or margin.
6. The method in claim 1, wherein the processing step includes
processing the normalized private financial data with public
trading market and trading reference data to calculate profit and
loss based on underlying market prices or to calculate margin
requirements.
7. The method in claim 1, wherein the processing step includes
associating the normalized private financial data to entitled
entities.
8. The method in claim 1, wherein the information processing system
is a scalable cloud computing and storage system.
9. The method in claim 8, wherein the scalable cloud computing and
storage system supports multiple participant accounts which are
entitled to receive selected portions of the enhanced private
financial data, and wherein one of the participant accounts
provides selected portions of the enhanced private financial data
to one or more recipients external to the scalable cloud computing
and storage system.
10. The method in claim 8, wherein the scalable cloud computing and
storage system supports multiple participant accounts which are
entitled to receive selected portions of the enhanced private
financial data, wherein the scalable cloud computing and storage
system supports multiple vendor accounts, and wherein one of the
participant accounts provides selected portions of the enhanced
private financial data to one of the vendor accounts for further
processing after which the one vendor account performs the
distributing step.
11. The method in claim 1, further comprising receiving a real time
query from an entitled entity and providing enhanced private
financial data in real time responsive to the real time query to
the entitled entity.
12. The method in claim 1, further comprising automatically
removing private financial data from the storage associated with
the information processing system at a predetermined expiration
time.
13. The method in claim 1, further comprising encrypting the
normalized private financial data, and wherein the storing step
includes storing the encrypted normalized private financial data in
secure storage.
14. The method in claim 1, wherein the distributing step includes
distributing selected portions of the enhanced private financial
data to one or more financial regulatory entities.
15. An information processing system comprising: one or more data
processors in communication with a cloud computing and storage
platform via a communications network, data storage coupled to the
one or more processors, wherein the one or more data processors are
configured to: receive from multiple data sources private financial
data related to financial articles of trade that are tradable on
one or more financial markets, where private financial data is
exclusively accessible by only a set of entitled entities
associated with the private financial data or transactions
involving the private financial data, and where the received
private financial data from the multiple data sources includes
multiple different data formats; normalize the private financial
data in multiple different data formats received from the multiple
data sources into a normalized data format; store the normalized
private financial data in the cloud computing and storage platform;
process or direct the cloud computing and storage platform to
process the normalized private financial data with public financial
data to provide enhanced private financial data and thereafter
store the enhanced private financial data in the cloud computing
and storage platform; and direct the cloud computing and storage
platform to distribute selected portions of the enhanced private
financial data to entitled entities with legitimate rights to the
selected portions of the enhanced private financial data.
16. The information processing system in claim 15, wherein the one
or more data processors are configured to encrypt the normalized
private financial data before storing in the cloud computing and
storage platform.
17. The information processing system in claim 15, wherein the one
or more data processors are configured to process the normalized
private financial data with other, more static private financial
data that includes one or more of: custom haircuts, allocation
relationships, mappings between clearing and trading accounts,
overnight positions, posted collateral, or margin.
18. The information processing system in claim 15, wherein the one
or more data processors are configured to process the normalized
private financial data with public trading market and trading
reference data to calculate profit and loss based on underlying
market prices or to calculate margin requirements.
19. The information processing system in claim 15, wherein the one
or more data processors are configured to associate the normalized
private financial data to entitled entities.
20. A financial information processing system comprising: one or
more data processors coupled to one or more participant interfaces;
a cloud computing and storage platform; and one or more
communications networks providing data communication for the one or
more data processors and the cloud computing and storage platform;
wherein the one or more data processors are configured to: receive,
via the one or more communication networks, from multiple data
sources private financial data related to financial articles of
trade that are tradable on one or more financial markets, where
private financial data is exclusively accessible by only a set of
entitled entities associated with the private financial data or
transactions involving the private financial data, and where the
received private financial data from the multiple data sources
includes multiple different data formats; normalize the private
financial data in multiple different data formats received from the
multiple data sources into a normalized data format; store the
normalized private financial data in the cloud computing and
storage platform; and process or direct the cloud computing and
storage platform to process the normalized private financial data
with public financial data to provide enhanced private financial
data and thereafter store the enhanced private financial data in
the cloud computing and storage platform, wherein the cloud
computing and storage platform is configured store the normalized
private financial data and to distribute selected portions of the
enhanced private financial data to entitled entities with
legitimate rights to the selected portions of the enhanced private
financial data.
21. The financial information processing system in claim 20,
wherein the cloud computing and storage platform is configured to
support multiple participant accounts which are entitled to receive
selected portions of the enhanced private financial data, and
wherein one of the participant accounts is configured to provide
selected portions of the enhanced private financial data to one or
more recipients external to the cloud computing and storage
platform.
22. The financial information processing system in claim 20,
wherein the cloud computing and storage platform is configured to
support multiple participant accounts which are entitled to receive
selected portions of the enhanced private financial data, wherein
the cloud computing and storage platform is configured to support
multiple vendor accounts, and wherein one of the participant
accounts is configured to provide selected portions of the enhanced
private financial data to one of the vendor accounts for further
processing after which the one vendor account performs the
distributing step.
23. The financial information processing system in claim 20,
wherein the cloud computing and storage platform is configured to
receive a real time query from an entitled entity and provide
enhanced private financial data in real time responsive to the real
time query to the entitled entity.
24. The financial information processing system in claim 20,
wherein the cloud computing and storage platform is configured to
distribute selected portions of the enhanced private financial data
to one or more financial regulatory entities.
25. The financial information processing system in claim 20,
wherein the cloud computing and storage platform is configured to
automatically remove private financial data from storage at a
predetermined expiration time.
26. The financial information processing system in claim 20,
wherein the cloud computing and storage platform includes a secure,
regulated data storage and wherein the cloud computing and storage
platform is configured to store encrypted normalized private
financial data in the secure, regulated data storage.
27. The financial information processing system in claim 20,
wherein the private financial data includes one or more of:
transactional data, margin requirements or calls, trading position
information, ownership information, or a status of third parties as
guarantors of, or providers of credit for, specified
transactions.
28. The financial information processing system in claim 20,
wherein the public financial data includes one or more of: trading
market data, trading reference data, an identity of regulators with
a legal right to private financial data as authorized by owners of
the private financial data, or guarantors or creditors of
transactions giving rise to private financial data.
29. The financial information processing system in claim 20,
wherein the multiple data sources include multiple public trading
exchanges and one or more dark pools, and wherein the private
financial data is provided in drop copy files associated with the
multiple public trading exchanges and the one or more dark pools.
Description
[0001] This application claims priority and incorporates by
reference U.S. provisional patent application Ser. No. 61/577,188,
filed on Dec. 19, 2011.
TECHNOLOGY OVERVIEW
[0002] The technology aggregates, stores, and preferably
selectively processes private data, e.g., private financial data,
from disparate sources to provide enhanced management, storage, and
directed delivery of such data for users.
BACKGROUND
[0003] Changes in market dynamics, such as the increasing speed at
which trading occurs and the resulting speed in which "bad things"
can happen, (e.g., a 1,000 point plunge in the Dow Jones Industrial
Average--about 9%--in less than 10 minutes only to recover those
losses within minutes), require that data be collected and
available for analysis within an ever decreasing period of time and
to a wider array of parties who have a legitimate interest in the
data. What used to be acceptable to do the next day, at the end of
the current day, or even at regular intervals throughout the day
must be completed in real-time throughout the day and made
available to an expanded group of people who have a legitimate
interest in different elements or combinations of financial trading
related data from a large number of disparate sources of such
data--all within a very brief period of time. These disparate
sources employ different data formats and protocols making
centralized and effective use difficult. As a result, pre-existing
methods of storing, accessing, managing different elements or
combinations of financial trading related data from a large number
of disparate sources of such data, and delivering appropriate data
to specified recipients, who may be within and/or outside of a
designated organization and entitled to see different elements
and/or combinations of such data, are quite slow and often
ineffective.
SUMMARY
[0004] Non-limiting example embodiments include methods and systems
for acquiring and aggregating private financial data from multiple
disparate sources. The private financial data is normalized,
aggregated, preferably enhanced, and stored in secure storage.
Entitled entities may retrieve selected private financial data from
that secure storage efficiently, flexibly, and rapidly. Examples of
financial private data include non-liquidity destination related
sources of private data as well as liquidity destination related
sources. A non-limiting example of a computer-implemented,
consolidated, private financial data service is based on a secure,
permission-based, aggregated and consolidated data cloud, which
enables provision/distribution to one or more authorized parties
with legitimate interests selected portions of the consolidated,
private financial data.
[0005] An information processing system having multiple processors
receives from multiple data sources private financial data related
to financial articles of trade that are tradable on one or more
financial markets. Private financial data is exclusively accessible
by only a set of entitled entities associated with the private
financial data or transactions involving the private financial
data. The received private financial data from the multiple data
sources includes multiple different data formats. The information
processing system normalizes the private financial data in multiple
different data formats received from the multiple data sources into
a normalized data format and stores the normalized private
financial data in storage associated with the information
processing system. The normalized private financial data is
processing using public financial data to provide enhanced private
financial data, which is stored. In a preferred but example
embodiment, the normalized private financial data is encrypted
before storage. The information processing system distributes
selected portions of the enhanced private financial data to
entitled entities with legitimate rights to the selected portions
of the enhanced private financial data. As a non-limiting example,
the distributing step includes distributing selected portions of
the enhanced private financial data to one or more financial
regulatory entities.
[0006] Examples of the private financial data includes one or more
of: transactional data, margin requirements or calls, trading
position information, ownership information, or a status of third
parties as guarantors of, or providers of credit for, specified
transactions. Examples of public financial data include one or more
of: trading market data, trading reference data, an identity of
regulators with a legal right to private financial data as
authorized by owners of the private financial data, or guarantors
or creditors of transactions giving rise to private financial data.
Examples of multiple data sources include multiple public trading
exchanges and one or more dark pools, and wherein the private
financial data is provided in drop copy files associated with the
multiple public trading exchanges and the one or more dark
pools.
[0007] In example embodiments, the normalized private financial
data is processed with other, more static private financial data
that includes one or more of: custom haircuts, allocation
relationships, mappings between clearing and trading accounts,
overnight positions, posted collateral, or margin. In example
embodiments, the normalized private financial data is processed
with public trading market and trading reference data to calculate
profit and loss based on underlying market prices or to calculate
margin requirements. The normalized private financial data is
preferably associated to entitled entities.
[0008] In example embodiments, the information processing system is
a scalable cloud computing and storage system that supports
multiple participant accounts which are entitled to receive
selected portions of the enhanced private financial data. One of
the participant accounts provides selected portions of the enhanced
private financial data to one or more recipients external to the
scalable cloud computing and storage system. In example
implementations, the scalable cloud computing and storage system
supports multiple participant accounts which are entitled to
receive selected portions of the enhanced private financial data.
It also supports multiple vendor accounts. One of the participant
accounts provides selected portions of the enhanced private
financial data to one of the vendor accounts for further processing
after which the one vendor account performs the distributing
step.
[0009] In another example embodiment, a real time query is received
from an entitled entity and enhanced private financial data is
provided in real time responsive to the real time query to the
entitled entity.
[0010] In another example embodiment, private financial data is
automatically removed from the storage associated with the
information processing system at a predetermined expiration
time.
[0011] Another aspect of the technology includes a financial
information processing system that includes one or more data
processors coupled to one or more participant interfaces, a cloud
computing and storage platform, one or more communications networks
providing data communication for the one or more data processors
and the cloud computing and storage platform. The one or more data
processors are configured to: [0012] receive, via the one or more
communication networks, from multiple data sources private
financial data related to financial articles of trade that are
tradable on one or more financial markets, where private financial
data is exclusively accessible by only a set of entitled entities
associated with the private financial data or transactions
involving the private financial data, and where the received
private financial data from the multiple data sources includes
multiple different data formats; [0013] normalize the private
financial data in multiple different data formats received from the
multiple data sources into a normalized data format; [0014] store
the normalized private financial data in the cloud computing and
storage platform (e.g., in a secure, regulated data storage in
encrypted format); and [0015] process or direct the cloud computing
and storage platform to process the normalized private financial
data with public financial data to provide enhanced private
financial data and thereafter store the enhanced private financial
data in the cloud computing and storage platform, [0016] wherein
the cloud computing and storage platform is configured store the
normalized private financial data and to distribute selected
portions of the enhanced private financial data to entitled
entities with legitimate rights to the selected portions of the
enhanced private financial data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIG. 1 illustrates an example embodiment of a system for
receiving, aggregating, normalizing, storing, and managing private
data from multiple disparate private data sources.
[0018] FIG. 2 depicts an example computer system for receiving,
aggregating, normalizing, storing, and managing private data from
multiple disparate private data sources.
[0019] FIG. 3 illustrates a flow chart illustrating example
procedures for receiving, aggregating, normalizing, storing, and
managing private data from multiple disparate private data
sources.
[0020] FIG. 4 illustrates conceptually an example cloud computing
system.
[0021] FIG. 5 illustrates an example embodiment of a system for
receiving, aggregating, normalizing, storing, and managing private
data from multiple disparate private financial data sources.
[0022] FIGS. 6-8 are function block diagrams illustrating
non-limiting example embodiments of the technology applied to
different security trading related services scenarios drawing
information from multiple disparate security trading data
sources.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0023] This application discloses computer-implemented arrangements
and methods. In the following description, for purposes of
explanation and not limitation, specific details are set forth such
as particular architectures, interfaces, techniques, etc., in order
to provide a thorough understanding of the present invention. Those
skilled in the art will appreciate that the functions of those
blocks may be implemented using individual hardware circuits, using
software programs and data in conjunction with one and typically
multiple suitably programmed microprocessor or general purpose
computers that may also include application specific integrated
circuitry (ASIC) and/or digital signal processors (DSPs). Software
program instructions and data are stored on computer-readable
storage, and when the instructions are executed by a computer or
other suitable processor control, the computer or processor
performs the functions. Databases and database query technology may
be used to storage and access data, e.g., as tables, relational
databases, object-based models, and/or distributed databases as
well as other formats. Although process steps, algorithms or the
like may be described or claimed in a particular sequential order,
such processes may be configured to work in different orders. In
other words, any sequence or order of steps that may be explicitly
described or claimed does not necessarily indicate a requirement
that the steps be performed in that order. The steps of processes
described herein may be performed in any order possible. Further,
some steps may be performed simultaneously despite being described
or implied as occurring non-simultaneously (e.g., because one step
is described after the other step). Moreover, the illustration of a
process by its depiction in a drawing does not imply that the
illustrated process is exclusive of other variations and
modifications thereto, does not imply that the illustrated process
or any of its steps are necessary to the invention(s), and does not
imply that the illustrated process is preferred. A description of a
process is a description of an apparatus for performing the
process. The apparatus that performs the process may include, e.g.,
one or more data processors and those input devices and output
devices that are appropriate to perform the process.
[0024] Also, various forms of computer readable media may be
involved in carrying data (e.g., sequences of instructions) to a
processor. For example, data may be (i) delivered from RAM to a
processor; (ii) carried over any type of transmission medium (e.g.,
wire, wireless, optical, etc.); (iii) formatted and/or transmitted
according to numerous formats, standards or protocols, such as
Ethernet (or IEEE 802.3), SAP, ATP, Bluetooth, and TCP/IP, TDMA,
CDMA, 3G, etc.; and/or (iv) encrypted to ensure privacy or prevent
fraud in any of a variety of ways well known in the art.
[0025] Many business transactions around the world are executed
using digital representations of financial articles of trade
residing in computer systems maintained by financial services
corporations, and example non-limiting embodiments described below
are directed to financial articles of trade to facilitate
description, where each represents a financial interest/asset or
the right to acquire a financial interest/asset. A financial
article of trade is fungible in nature (i.e., equally recognizable
and accepted at a variety of places and/or by a variety of parties)
and delivered (or communicated/represented) electronically such
that there is no (or very minimal) cost or degradation in value
from transfer from one physical or logical location to another.
Example financial articles of trade include, but are not limited
to, rights related to cash, credit, equity securities, debt
securities, commodities, futures, options, swaps, foreign exchange
values, balances, rates, etc. Financial articles of trade are
traded on various complex financial markets with increasingly
diverse, numerous, and non-integrated sources of private and
non-private data associated with large numbers of disparate trading
inputs, processes, and various outputs.
[0026] Public data is accessible to anyone and that access may be
free or require a fee for access. Non-limiting examples of public
financial data includes generalized, market-wide trading data or
trading reference data. Private data is data that is not public or
accessible to market participants generally through commercial or
non-commercial arrangements, but rather, is accessible only by the
owners of, or third parties who have been authorized to receive
access to, such data. In contrast, market data, for example, can be
commercially acquired by anyone.
[0027] Private data includes confidential and proprietary data
specific to individuals associated with the data and does not need
to be disclosed publicly for regulatory, compliance, or other
reasons. Private financial data, such as transactional data, margin
requirements or calls, trading position information, etc., is
exclusively accessible by only a set of entitled entities
throughout the trade process. Private data is useful for a wide
variety of diverse purposes such as trading/hedging, credit, risk
management, internal reporting/analysis/compliance, external
regulatory reporting/analysis compliance, display, and viewing.
Entities are entitled to access or receive private data only
through association with the data or the transactions associated
with the data. Such an association may include origination of the
data, ownership of the data, serving as the guarantor of
transactions associated with the data or authorized regulator of
transactions associated with the data, or any other legal or
structural association with the data that entitles a party to
access the data, e.g., if the owner of the data assigns certain
rights to an entity which requires, and is thus entitled, to access
the data.
[0028] Private financial data from disparate sources are usually
formatted differently and may be accessed and/or transmitted using
different protocols. Thus, there is a need for technology that can
aggregate and normalize private financial data from disparate
sources as compared to a private data base comprised of data
generated by a common originator(s)/owner(s) of data and accessible
by the common originator(s)/owner(s). Plus, there is also a need
for technology to securely store, provide access, and distribute
selected portions of aggregated, normalized, and preferably
enhanced private financial data using computer communication
network(s) in a way that ensures only authorized entities are
granted access, use, etc., e.g., based on authorization rights
attributable to each party. Entitled entities are for instance
entitled through association including origination of data, owner
of data, any guarantor of transactions associated with the data,
any oversight body over the transactions associated with the data.
Non-limiting examples of authorized entities in the securities
trading applications example, also called participants, include
trading firms, brokers, trading members, regulators, investment
banks, clearing firms, Central Clearing Parties (CCPs), General
Clearing Members (GCMs), an end client, and others.
[0029] Example embodiments enable computer-implemented collection
and aggregation of private information from a variety of disparate
sources, normalization of the collected data, protected storage,
and management of the stored data to permit retrieval by and/or
distribution to with analysis and/or enhancement of selected
portions of the private data either by itself or in combination
with other data (public or private) to facilitate controlled access
to the different entities involved.
[0030] FIG. 1 shows one non-limiting example embodiment system
configured to collect or receive private data from many disparate
and non-disparate sources of private data represented in simplified
fashion as 14a (including information designated A) and 14b
(including information designated B). Examples of private data
include, but are not limited to, drop copy services at exchanges,
margin requirements services at CCPs, other brokers and clearing
firms with give up trades, etc. Furthermore, the private data may
be collected, or received, in any kind of formatted data format,
typically as standard data messages, data feeds, or in any other
for the purpose suitable format.
[0031] The aggregated private data is consolidated in an elastic
data mart 10 shown as a data cloud. An elastic data mart is a
database, or a collection of databases, designed to help users make
strategic decisions. One or more computers may enrich the private
data with public data 12 (including information designated C) such
as market data, initial margin data, etc. and with static private
data 13 (including information designated D) such as reference
data, mappings, custom haircuts, etc. Only entitled entities 11a-d
may be recipients of the private data or enriched private data.
Entitled recipients then receive the consolidated and enriched
private data for the purpose of trading/hedging, credit management,
risk, internal reporting/analysis/compliance, external regulatory
reporting/analysis compliance, and viewing/display. The technology
in this application advantageously distributes and/or allows
retrieval of certain portions of stored information to different
recipients based on the specific portions that each recipient is
authorized to receive. In the example shown in FIG. 1, Internal
Recipient 1 is authorized to receive information elements A, B, C,
and D, whereas Internal Recipient 2 is authorized to receive
information elements A, B, and C, External Recipient 1 is
authorized to receive information elements A and D, and External
Recipient 2 is authorized to receive information elements B and
C.
[0032] FIG. 2 illustrates an example embodiment of a financial data
aggregation and management computer system 20 including one or more
processors 22, one or more memories 23, one or more inputs/outputs
("I/O(s)") 24, one or more network interfaces 25, and one or more
storage devices 26, all of which operate collectively to execute
computer program instructions. One of ordinary skill in the art
will recognize that a computer-implemented, financial private data
aggregation, storage, and processing system may contain additional
components and that FIG. 2 is a high level representation of some
of the components and processes of such a computer system for
illustrative purposes.
[0033] FIG. 3 illustrates a flow chart illustrating example
computer-implemented procedures for receiving, aggregating,
normalizing, storing, retrieving, enhancing, and providing private
enhanced data from multiple disparate private data sources. In step
S1, a computer-implemented data storage and processing system
gathers, aggregates, and normalizes private financial data from
multiple disparate or heterogeneous sources that may use different
protocols and formats. Example private data may be received over
electronic interfaces from different sources such as for example
trading exchanges, dark pools (trading volume or liquidity that is
not openly available to the public), and clearing
firm/broker/trader logs, clearing houses, etc.
[0034] The data may be received in a variety of forms including via
a communications network (wired or wireless), computer-readable
storage media, operator input, live or batch data feeds, etc. The
format of the data received typically varies. Examples include
financial industry protocols like the FIX protocol, clients
streaming private data via proprietary protocols, through messaging
middleware, or other formats. The variously formatted received data
is normalized before storage so that it can be aggregated and
processed efficiently. As an example, stock for the same company
may be purchased and sold at different liquidity destinations using
different stock symbols. Statistics for these different stock
symbols such as asking price and number of shares are normalized
into a comparable and consistent format and then aggregated
together. Normalization ensures the private data is converted into
comparable and consistent formats and that the data fields contain
valid values (data validation). Detailed normalization examples are
provided below. The normalized private data is stored in a central
or distributed database. One example is a real-time flexible
datamart database or collection of databases designed to help
managers make strategic decisions about their business. Another
example is a data warehouse which combines databases across an
entire enterprise. In a preferred example embodiment, the datamart
is scalable (by being able to dynamically add more machines to the
data cloud/cluster) to a very large size in a cloud storage
infrastructure with no degradation of performance.
[0035] Once the information is gathered and normalized into a
comparable and consistent format, it is then aggregated or grouped
together based on common characteristics and information elements.
Aggregation of the private data may be performed via an internal,
third party, or "entitled entity" provided network infrastructure.
The aggregation is handled across multiple machines connected on
the network.
[0036] In step S2, the computer-implemented system processes the
normalized data to provide some enrichment or enhancement of some
or all of the normalized and aggregated private data. For example,
selected portions of normalized financial data may be processed
with certain public financial data (market data and reference data)
to allow for example calculation of intra-day profit and loss based
on underlying market prices or of margin requirements based on
exchange-provided initial margin factors on a per financial article
of trade basis. Enrichment of the private financial data provides
relevant and useful associations between private data to be made
which may be used later by entitled entities to deliver data
services based on the stored private data. Associations may for
example identify relationships between trade transactions and/or
parties associated with the transactions. Other examples of
enhancement data includes financial reference data, additional
financial instrument identifiers/symbologies, "greek" values often
used in options and derivatives trading at the time an order was
placed, etc. This step may also include enrichment of private
financial data with static private financial data example of which
include custom haircuts, allocation relationships, mappings between
clearing and trading accounts, overnight positions, posted
collateral or margin, etc. One example of leveraging static private
data to provide a more detailed analysis of relationships between
disparate data elements and associated factors is categorizing
trade transactions by the venues they relate to or within the
electronic/logical market segments they belong to for that
venue.
[0037] In step S3, the computer-implemented system maps and
analyzes private financial data across multiple assets,
multi-markets, clearing houses, currencies, multi-trading systems,
global scope and/or regions. Step S2 adds information into the
financial data aggregation and management system so as to
facilitate potential analysis. Step S3 performs analysis using
elements of such additional data. Analyze private data and further
enrich with public data for better analysis. For example, enriching
the message with public market data of what the related instruments
were trading/quoting at when the transaction was submitted.
[0038] The security of the private data is maintained, e.g., via
encryption by the computer-implemented system, (step S4), while
consolidating the data in the system, which again may be a
consolidated elastic or scalable datamart.
[0039] Step S5 includes optional real-time querying of the datamart
by a user or participant for a particular portion of the aggregated
and possibly enhanced private financial data for which such party
is expressly authorized. For example, a bank that has agreed to
extend credit to finance trades submitted by a client may be
authorized to view only those transactions consummated by such
client using credit extended by the bank.
[0040] Step S6 provides an optional ad-hoc analytics/reporting step
so that parties with legitimate rights to certain private financial
data in the datamart (based on permission, password, encryption
key, etc.) define their own reports, have those reports delivered
securely, and at what frequency they want to receive those reports
(daily, monthly, just one time, etc.) For example, a regulator may
be authorized to receive reports of trading activity in a certain
stock which involve trading volumes and/or values within a
specified time prior to a public announcement by the company that
issues such stock which historically have been shown to involve
illegal use of "insider information."
[0041] Parties with legitimate rights to private financial data in
the consolidated datamart may request or subscribe to a real-time
data stream of consolidated and aggregated private data for further
analysis by computer program-implemented algorithms, either on
their own computer system or performed by a third party computer
system, or for display and viewing by the party (optional step S7).
For instance via a user-defined delivery mechanism (e.g., email,
ftp, sftp) may be used to define where and how to send the report
to the client.
[0042] Optional step S8 provides support for business intelligence
integration. For example, computer-implemented business
intelligence tools implemented by the user or by a third party may
be used to query and then present to entitled entities private data
received from the datamart. Examples include data services tools
including query functionalities, reporting services, custom on
demand analytics tools, and real-time subscription tools. This may
involve several steps including an authentication step so that
users may only submit queries related to their own private data,
defining and submitting queries to pull out private data based on
user defined criteria, and formatting the query results in a user
defined report layout. A user specified encryption key may be used
at the datamart to encrypt the resulting report for delivery to the
client.
[0043] The above may be executed on a cloud computing network,
which is a highly-scalable, dynamic service, allowing cloud
computing providers to provide resources to customers over various
communication connections. The cloud infrastructure provides a
layer of abstraction, such that customers do not require knowledge
of the specific infrastructure within the cloud that provides the
requested resources. For example, systems such as infrastructure as
a service (IaaS), allow customers to rent computers on which to run
their own computer applications. Such systems allow scalable
deployment of resources, wherein customers create virtual machines,
i.e., server instances, to run software of their choice. Customers
can create, use, and destroy these virtual machines as needed, with
the provider usually charging for the active servers used to
seamlessly incorporate the resources allocated to a customer in the
cloud network into a customer's existing private enterprise
network.
[0044] FIG. 4 illustrates conceptually a general example of a cloud
computing and storage system. Cloud computing uses computing
resources (hardware and software) that are delivered as a service
over a network (e.g., the Internet). Cloud computing entrusts
remote services with a user's data, software, and computation, and
users are provided access to application software and databases.
The cloud provider manages the infrastructure and platforms on
which the applications run. End users may access cloud-based
applications through a web browser, a light-weight desktop, mobile
application, or other computer-implemented device. Benefits of
cloud computing includes agility by improving users' ability to
re-provision technological infrastructure resources; application
programming interface (API) accessibility to software that enables
machines to interact with cloud software in the same way the user
interface facilitates interaction between humans and computers;
reduced cost because the infrastructure is typically provided by a
third-party and does not need to be purchased for one-time or
infrequent intensive computing tasks; device and location
independence to enable users to access systems using a web browser
regardless of their location or what device they are using (e.g.,
PC, mobile phone); virtualization technology allows servers and
storage devices to be shared and utilization be increased and
applications can be easily migrated from one physical server to
another; redundancy; scalability and elasticity via dynamic
provisioning of data storage and data processing resources.
[0045] FIG. 5 illustrates an example embodiment of a cloud
computing and storage system 50 for receiving, aggregating,
normalizing, enhancing, and storing private financial data from
multiple disparate private financial data sources 40 including NYSE
drop copy, NASDAQ drop copy, BATS drop copy, dark pool drop copy,
and other data sources such as custom back office data files.
Financial data from the multiple disparate private financial data
sources 40 is received at a provider data center and temporarily
stored in an electronic inbox 42 implemented for example using
buffer and/or other short term memory. The buffered financial data
is then normalized and aggregated 44, possibly processed or
enhanced 45, preferably compressed for storage 46, and preferably
encrypted 48 before being provided via one or more networks to a
private data store database 52 in the cloud computing and storage
system 50. The processing stages may be performed for example using
one or more computer servers associated with the provider data
center 41. The cloud computing and storage system 50 also includes
public data storage 53 accessible to the enhancement processing
stage 45 in the provider data center 41 and to enhancement
processing, such as searching and/or analytics, performed by some
data processing resources 56 of the computing and storage system 50
allocated to process and enhance the selected (e.g., queried)
portions of private financial data from the private data storage 52
and providing same to a participant account storage 58. Participant
refers to an authorized/legitimate entity for which an account is
established in the computing and storage system 50 and which
receives selected private financial information.
[0046] The following is a detailed non-limiting example use case
where private financial data is received from multiple disparate
sources. The provider data center may collect data from a broker's
internal securities order management system(s), securities
exchanges such as NASDAQ, NYSE, Direct Edge, BATS, dark pool
providers such as Credit Suisse Cross Finder or JP Morgan's JPMX
and normalizes and aggregates the data before storing it on behalf
of the Participant (in the examples, a "participant" is a
securities broker).
[0047] Assume that in this non-limiting example use case that the
goal for the participant is to receive a private financial data
from the private data store 52 for use in creating an ad hoc trade
blotter type report for the participant staff. The reports are
required for immediate review to address a participant customer
question or issue. The customer's private financial data is
therefore needed to produce the desired report. That private
financial data includes in this example customer trades of
securities via two internal participant order management systems
(one for access to a participant smart order router while the other
is used for routing of securities orders to a specific trader and
desk at the participant) and also via direct exchange connections
to NASDAQ, BATS and NYSE separate and devoid of the participants
physical network and order management infrastructure. As a result,
the customer's trade data is processed by a total of five separate
disparate sources. The provider data center collects from the
exchanges and the participant, all of the messages requiring
normalization via electronic drop copies or, as an alternative,
flat files.
[0048] The received financial data is normalized by electronically
parsing securities transactions from electronic messages received
from those disparate sources to identify the existence and
placement of specific data fields in each message and build a map
of these fields to a single common data structure. This common data
structure is referred to as a normalized data store. More
specifically in this example, received data formats are normalized
into JavaScript Object Notation (JSON) objects, which are discrete
pieces of data stored in JSON format. A JSON format is a
lightweight data-interchange format that is easy for humans to read
and write, and for machines to parse and generate. JSON is based on
the object notation of the JavaScript language, but does not
require JavaScript to read or write because it is a text format
that is language independent. A JSON record is a complete set of
data JSON objects stored in JSON format. Certain fields are
required on every record such as fields used for explicit
search/indexing on trade blotters. A trade blotter report includes
a record of trades and details of the trades made over a period of
time (e.g., one trading day). The details of a trade may include
such things as the time, price, order size, and a specification of
whether it was a buy or sell order. The blotter is usually created
through a computer-implemented trading software program that
records the trades made through a data feed. An arbitrary number of
additional fields (zero or more) can be part of every record, and
do not need to be the same for every record in the system. This
allows participant clients to add proprietary data/fields to their
records, and also accommodates for the lack of consistency across
trade order entry protocols.
[0049] The JSON objects are then partitioned into files such that
all orders in a given order chain are in the same file but also in
a way that any individual file does not exceed a target size. This
target size is set to facilitate parallel processing across
multiple data processing nodes in a cloud cluster, and also so at
given point in time during a search, the memory requirements for a
given node are reasonable. This allows the cloud system 50 to scale
to searching archives that could extend into the petabyte or
exabyte range.
[0050] Those files of JSON records are then compressed and finally
encrypted, e.g., using an AES-256 encryption key. Encryption keys
are preferably rotated per client, per day, so that if a key is
compromised, at most one day of one client's data is at risk. This
also has the benefit of limiting how much data needs to be
re-encrypted if a key revocation process must be performed. The
encrypted, compressed, and normalized private financial data is
then stored a regulated private data store 52 and made available,
in real time or batch methods, for use by a participant authorized
to access the regulated private data store 52 within the cloud
system 50. An example participant might be a securities broker like
Goldman Sachs.
[0051] Upon request or schedule, specific data (as defined by the
participant) is moved from the regulated private data store 52 to
an environment specified by the participant which might be a
participant account 56, 58 in the cloud 50 or a dedicated computer
infrastructure. This enables the participant to use the normalized
data within its own applications examples of which might be
transaction cost analysis, transaction surveillance, transaction
risk management, portfolio stress testing etc.
[0052] A participant logs in (authenticates) to the private data
storage system, e.g., through either an online, web-delivered
Graphical User Interface (GUI) or an Application Programming
Interface (API), and enters a search/retrieval request for private
financial data. For example, the participant requests that the
provider data center deliver a file, containing specific message
files (trade date (MM/DD/YYYY, side (buy, sell, sell short), ticker
symbol, quantity (number of shares), price(securities transaction
executed price), client identifier(account number), transaction ID,
etc. That search is then performed across all JSON records in the
cloud system for that client to locate all records matching the
search criteria (transactions meeting the participant-defined
attributes having occurred across all five of the original trading
systems). Files of JSON objects are decrypted and decompressed in
memory (so decrypted data is never persisted to disk), and then the
JSON records are checked to see if they meet the search criteria.
All matching records are copied into a result set of files, and
these result set files are compressed and encrypted before delivery
to the participant.
[0053] The retrieved result set files are copied down to a delivery
process, protected behind multiple layers of network security,
where the files are decrypted, decompressed, and the results are
merged into a single result file. That result file is then
compressed, and optionally encrypted with a PGP encryption key
(participants may choose to have their results encrypted or not).
The result file is then delivered to wherever the participant
specifies. For example, the result file may be FTP/SFTP to a
participant's server, use the server-client protocol SCP to deliver
the file, or the result file may be stored in the cloud storage in
a storage account owned by the participant.
[0054] The participant receives the file and conducts research
based upon it. The participant may execute various processing of
that private financial data, including internal processing, or
making that file available to another vendor, who may provide some
processing service for that data. In the examples in FIGS. 6-8
described below, the exchange of information between cloud
participants and vendors may be facilitated by provisioning and
permissioning storage "buckets" or containers on the storage
platform so that vendors can read participant data delivered by the
provider data center 41 to buckets designed for that purpose. In
this way, the provider data center 41 can facilitate selecting
subsets of private financial data for that participant from the
private data storage and make that private data readily available
to vendors to provide services using that data all within the cloud
platform.
[0055] In another non-limiting example use case, a participant
requires a scheduled delivery of a defined set of private data from
the private data store 52 in the cloud for use in a nightly batch
process, e.g., a billing file for customer invoices. The private
data includes transactional records for many customers sourced from
many disparate systems and stored in the private data store 52. The
participant defines a scheduled delivery, e.g., at 8 pm, of all
data for all customers fitting a defined criteria to the
participant. The private data is delivered, in normalized form, to
the participant at the scheduled time. This example use case may be
implemented as in the earlier example use case with the addition of
a recurring search/retrieval request, where the search criteria are
relative to the date/time of when the recurrence pattern repeats
(e.g., always select data from 8:00 am to 4:00 pm on the day the
search runs). The participant specifies a recurrence pattern such
as Monday through Friday, every hour on the hour, between 8:00 am
and 4:00 pm.
[0056] Another feature includes automatically removing private
financial data from the storage at a predetermined expiration time.
This is advantageous because keeping this data in storage involves
ongoing expenses which may no longer be justified given the passage
of time and reduced relevance of data that is no longer considered
current or timely.
[0057] FIGS. 6-8 are function block diagrams illustrating
non-limiting example embodiments of the technology applied to
different security trading related services scenarios drawing
information from multiple disparate security trading data sources.
In these figures, the cloud provider 50 may be for example the
Amazon Web Services (AWS) cloud platform and the secure cloud
platform 51 corresponds to NASDAQ OMX's FinQloud which provides
NASDAQ OMX built security, compliance and financial
industry-specific functionality to enable users to realize
substantial cost savings and achieve unprecedented flexibility over
traditional, on-premise computing models. FinQloud is a cloud
computing platform exclusively designed for the financial services
industry that combines Amazon Web Services' secure, flexible, and
cost-effective cloud infrastructure with NASDAQ OMX's experience in
providing technology platforms and advisory services for exchanges,
regulators, and broker-dealers to provide cost-effective and
efficient management and storage of financial data mandated by
regulation. FinQloud is a platform for efficient management and
storage of financial data that helps market participants streamline
operations and aids in management of regulatory compliance
requirements without making costly capital expenditures for their
underlying infrastructure. FinQloud provides the ability to
instantly provision and release compute, storage, and networking
resources as needed on a large scale to optimize cost-efficiency
while maintaining high performance. For FinQloud participants, this
means that resources are always available to meet an immediate
business need, no matter how great or how small.
[0058] The FinQloud secure platform 51 includes regulated private
data storage 52, public data storage 53, participant accounts 70
for multiple participants, and vendor accounts 82 in a vendor
marketplace 80. The regulated private data storage 52 includes
regulatory data storage 52a that is write once, read many (WORM)
type memory for storing encrypted private financial data, regular
data storage 52b for storing encrypted data, which may also be
private financial data but not in WORM memory, archival storage 52c
for archiving older encrypted data, and future storage 52d. Further
details of one example regulated data storage are provided in
commonly assigned U.S. provisional patent application 61/703,429,
filed on Sep. 20, 2012, entitled "Regulatory Records Retention (R3)
System and Method," the disclosure of which is incorporated herein
by reference. Each participant account 70 and each vendor account
82 has access to cloud computing, cloud storage, and other cloud
services. A provider data center 41, hosted in this example by
NASDAQ OMX, includes a participant "inbox" 60 for receiving private
financial information and query requests from a participant GUI 62.
The inbox 60 is coupled to one or more data processors 46, e.g.,
computer servers. A database memory 53 storing public data may also
be coupled to the data processors 46. The data processors 46
process the private financial data and optionally enhance it with
public data from storage 53 before encrypting the private financial
data and sending it to the regulated private data storage 52 for
secure storage.
[0059] In FIG. 6, the Participant may request that
specifically-identified private data be copied from the Regulatory
Data Storage (WORM) 52a and directed to the Participant's account
70 on the secure cloud platform 51 and/or copied or withdrawn from
Regular Data Storage (Encrypted, Non-WORM) 52b, Archival Storage
Medium 52c, or other Storage 52d, and directed to the Participant's
account 70 for computing, data storage, or other cloud services
within the overall secure cloud platform 51. Alternatively, the
Participant may choose to input information for computing, data
storage, or other cloud services within the Participant's account
70 from the Participant Inbox 60 and/or the Participant Graphical
User Interface (GUI) 62. The Participant may also direct
information from data storage or resulting from computing or other
cloud services associated with the Participant's account 70 to a
vendor account 82 located within a Vendor Marketplace 80 where such
vendor may provide services related to data storage, computing,
and/or other cloud services. The results of such vendor services
may be directed from the vendor account 82 back to the Participant
account 70. Information and other resources necessary for the
vendor to provide such services may originate external to the
secure cloud platform 51.
[0060] FIG. 7 illustrates a more detailed but still non-limiting
example of the scenario shown in FIG. 6 for the FinQloud platform
in which private financial data is directed to self-regulating
organizations (SROs) in an SRO secure zone 90. Example financial
type SROs include the Securities Exchange Commission (SEC), the
Financial Industry Regulatory Agency (FINRA), the Commodity Futures
trading Commission (CFTC), and/or Designated Examining Authorities
(DEAs), which are connected to respective data centers 92. A
Participant 70 directs that specified private data and/or private
data enhanced with public data be sent to one of several SROs who
have jurisdiction over the Participant's activities such as the
SEC, FINRA, CFTC, or DEAs. So rather than having to develop
"one-off" technical solutions to respond to regulatory
initiatives/requests of each of the SEC, FINRA, CFRC, or DEAs, the
secure cloud platform is used to direct specified information to
such organization(s) 90 which they can in turn make use of in their
own data center(s) 92. Although many of the function blocks are
similar to those in FIG. 6, the participant cloud inbox 60 is
located within the FinQloud platform in this example to enable the
Participant to input secure encrypted data into the Regulated
Private Data Store 52 without requiring that it be input via the
Provider Data Center 41.
[0061] The configuration for the non-limiting example scenario in
FIG. 8 is similar to that for the non-limiting example scenario in
FIG. 6. However, in FIG. 8, a Participant directs that specified
private data and/or private data enhanced with public data to be
sent to one or more recipients 100 who have legitimate rights to
such data but are not located within the secure cloud platform 51.
The Participant may instruct that this data be delivered to
specified recipients 100 directly or may enlist the assistance of a
vendor 82 to provide delivery. So rather than having to develop
"one-off" technical solutions to meet the disparate needs of
different recipients 100, a Participant leverages the capabilities
of the secure cloud platform to direct specified information to
each recipient 100 based on facts and circumstances applicable to
that recipient. Non-limiting examples include directing proxy
information to facilitate voting by corporate stockholder
recipients or directing tax information to investor client
recipients 100.
[0062] Various example embodiments, also related to a method of
sending packets received from a private data source to an entitled
destination in a cloud network. This may involve a logical client
edge router in a computer system receiving a packet from a private
source, querying a directory server for the destination's cloud IP
address and location IP address, encapsulating the received packet
when the entitled client edge router determines that the
destination is within the entitled space, further encapsulating the
received packet with the entitled destination's corresponding
location IP header, and forwarding the received packet to the
entitled destination, wherein the logical client edge router
forwards the received packet through the destination location IP
address to the destination cloud IP address.
[0063] Although the description above contains many specifics,
these should not be construed as limiting the scope of the claims
but as merely providing illustrations of example embodiments. For
example, even though many of the detailed example embodiments
relate to private and public financial data, those skilled in the
art will appreciate that the technology may also be applied to
other types of private and public data. It will also be appreciated
that the technology claimed fully encompasses other embodiments
which may become apparent to those skilled in the art, and that the
scope of the claims is accordingly not to be limited. Reference to
an element in the singular is not intended to mean "one and only
one" unless explicitly so stated, but rather "one or more." All
structural and functional equivalents to the elements of the
above-described embodiments that are known to those of ordinary
skill in the art are expressly incorporated herein by reference and
are intended to be encompassed hereby. Moreover, it is not
necessary for a device or method to address each and every problem
sought to be solved for it to be encompassed hereby. No element,
block, or instruction used in the present application should be
construed as critical or essential to the implementations described
herein unless explicitly described as such. Further, the phrase
"based on" is intended to mean "based, at least in part, on" unless
explicitly stated otherwise. Unclaimed subject matter is not
dedicated to the public and Applicant reserves all rights in
unclaimed subject matter including the right to claim such subject
matter in this and other applications, e.g., continuations,
continuations in part, divisions, etc.
* * * * *