U.S. patent application number 16/296393 was filed with the patent office on 2019-06-27 for corpus-scoped annotation and analysis.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to JOEL C. DUBBELS, MARK G. MEGERIAN, WILLIAM C. RAPP.
Application Number | 20190197062 16/296393 |
Document ID | / |
Family ID | 63143928 |
Filed Date | 2019-06-27 |
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United States Patent
Application |
20190197062 |
Kind Code |
A1 |
DUBBELS; JOEL C. ; et
al. |
June 27, 2019 |
CORPUS-SCOPED ANNOTATION AND ANALYSIS
Abstract
Corpus-scoped annotation and analysis. Enrichment analysis data
is generated including annotations and metadata for a plurality of
documents that are part of a corpus. Whether to generate a second
set of annotations is determined, based on a correlation of the
annotations and metadata. A relational database is populated with
the enrichment analysis data. A corpus-scoped query is resolved,
initiated by an application, using the enrichment analysis data and
content of the corpus.
Inventors: |
DUBBELS; JOEL C.;
(ROCHESTER, MN) ; MEGERIAN; MARK G.; (ROCHESTER,
MN) ; RAPP; WILLIAM C.; (ROCHESTER, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
63143928 |
Appl. No.: |
16/296393 |
Filed: |
March 8, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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15585223 |
May 3, 2017 |
10268688 |
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16296393 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/93 20190101;
G06F 40/169 20200101; G06F 16/9024 20190101; G06F 40/20 20200101;
G06F 16/2453 20190101 |
International
Class: |
G06F 16/93 20060101
G06F016/93; G06F 16/901 20060101 G06F016/901; G06F 17/27 20060101
G06F017/27; G06F 17/24 20060101 G06F017/24; G06F 16/2453 20060101
G06F016/2453 |
Claims
1. A computer program product comprising: a computer-readable
storage medium having program instructions embodied therewith,
wherein the computer-readable storage medium is not a transitory
signal per se, the program instructions comprising: program
instructions to produce a collection of prepared data, from
structured and unstructured data, utilizing rule-based and/or
machine learning techniques, wherein the structured and
unstructured data is provided by a content provider; program
instructions to produce a collection of vetted data, based on the
collection of prepared data, utilizing a subject matter expert to
create a dictionary of terms; program instructions to create a
trained algorithm based on the collection of vetted data; program
instructions to utilize the trained algorithm in combination with
at least one feature from the group consisting of: language
identification, semantic scoring, lemmatization, clustering and
classification, key phrase extraction, synonym expansion, document
and entity sentiment calculation, predictive analytics, and
trending entity identification to generate, by one or more computer
processors, enrichment analysis data, wherein the enrichment
analysis data includes annotations and metadata for a plurality of
documents that are part of a corpus; program instructions to
determine, by the one or more computer processors, whether to
generate a second set of annotations, based on a correlation of the
annotations and metadata; program instructions to determine, by the
one or more computer processors, whether to generate a second set
of annotations, utilizing a re-trained algorithm, wherein the
re-trained algorithm is created in response to updates to the
collection of vetted data; program instructions to determine
whether to update the enrichment analysis data with the second set
of annotations; program instructions to populate, by the one or
more computer processors, a relational database with the enrichment
analysis data, wherein the relational database is configured to
manage content of the documents and participate in incremental
ingestion during population of the relational database with
enrichment analysis data, and wherein the relational database is
configured to provide versioning capabilities for the enrichment
analysis data stored in the relational database; and program
instructions to resolve, by the one or more computer processors, a
corpus-scoped query initiated by an application using the
enrichment analysis data and content of the corpus, wherein
resolving the corpus-scoped query comprises optimizing, by the one
or more computer processors, the corpus-scoped query through a
query optimization feature of a relational database management
system for the relational database.
Description
FIELD OF THE INVENTION
[0001] The present invention relates generally to the field of
content analysis, and more particularly analyzing content in a
cognitive computational environment.
SUMMARY
[0002] Embodiments of the present invention provide systems,
methods, and computer program products for corpus-scoped annotation
and analysis. Enrichment analysis data is generated including
annotations and metadata for a plurality of documents that are part
of a corpus. Whether to generate a second set of annotations is
determined, based on a correlation of the annotations and metadata.
A relational database is populated with the enrichment analysis
data. A corpus-scoped query is resolved, initiated by an
application, using the enrichment analysis data and content of the
corpus.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a block diagram of a cognitive computing
environment, in accordance with an embodiment of the present
invention;
[0004] FIG. 2 is a block diagram of generated runtime artifacts
using an annotation store, in accordance with an embodiment of the
present invention;
[0005] FIG. 3 is a flowchart illustrating operational steps for
creating and storing enrichment analysis data, in accordance with
an embodiment of the present invention;
[0006] FIG. 4 is a block diagram of internal and external
components of the computer systems of FIG. 1, in accordance with an
embodiment of the present invention;
[0007] FIG. 5 depicts a cloud computing environment, in accordance
with an embodiment of the present invention; and
[0008] FIG. 6 depicts abstraction model layers, in accordance with
an embodiment of the present invention.
DETAILED DESCRIPTION
[0009] A cognitive computing environment may implement services to
generate a searchable corpus from structured, semi-structured,
and/or unstructured data. The corpus may be cognitively processed
by analytic engines to provide knowledge and insights relative to a
domain, a topic, a person, or an issue, based on training and
observations from all varieties, volumes, and velocities of data.
The analytic engines may create models to represent the domain and
generate and score hypotheses to answer questions and/or provide
further insight. For example, such analytic engines may rely on
rule-based learning as well as machine-learning to generate
analysis data for the corpus, whereby the analysis data may be used
by applications wishing to perform higher level cognitive
processing (e.g., knowledge and insights).
[0010] Typical cognitive computing environments handle annotations
and other analysis data for a corpus at a document granularity,
thereby making it difficult to search and analyze annotations at an
entire document-set level, or in a corpus-scoped manner. Typically,
methods for cognitive processing rely on techniques such as storing
the annotated context in a uniform annotation structure (e.g.,
Unstructured Information Management Architecture (UIMA.TM.) Common
Analysis System (CAS) Object, JavaScript Object Notation (JSON)
Object, etc.). All terms mentioned in this specification that are
known to be trademarks or service marks have been appropriately
capitalized. Use of such terms in this specification should not be
regarded as affecting the validity of the trademark or service
mark. UIMA.TM. is a trademark of the Apache Software Foundation
under Apache License 2.0. These techniques may provide a complete
representation of the annotations that exist in documents, and
later index the documents by keywords to enable a lookup using
keywords. If the documents are identified via a keyword search,
then the full annotation structure may be used to retrieve the
annotations for the identified documents.
[0011] Although typical cognitive computing environments enable
applications to handle higher level cognitive processing of a
corpus and a document-set level, the typical cognitive computing
environments do not easily provide applications with the ability to
determine relationships and trends across the entire corpus. For
example, a typical cognitive computing environment receive a query,
"which concepts frequently occur together or are in close
proximity?" In this example, the typical cognitive computing
environment may attempt to handle the query by inefficiently
analyzing each set of annotated documents within the corpus to
identify detailed annotations, and typically, the index of keywords
or annotations generated by the analytic engines do not include the
level of detail required for this type of query. Accordingly, it
may be advantageous to create a relational, indexed storage
solution for storing content, metadata and annotations for a corpus
in a normalized manner such that annotation search and analysis can
be performed in a comprehensive and corpus-scoped manner.
Furthermore, an instance of such a storage solution may result in
billions of annotations across millions of documents included in a
corpus.
[0012] Embodiments of the present invention provide methods,
systems, and computer program products for generating enrichment
analysis data including annotations and metadata for a plurality of
documents that are part of a corpus. Embodiments of the present
invention populate a relational database with the enrichment
analysis data, wherein the relational database is configured to
manage content, participate in incremental ingestion and provide
versioning capabilities. Furthermore, embodiments of the present
invention resolve a corpus-scoped query initiated by an application
using the enrichment analysis data and content of the corpus.
[0013] FIG. 1 is a block diagram of cognitive computing environment
100, in accordance with an embodiment of the present invention.
Cognitive computing environment 100 includes storage 110, analytic
engine 130, application 140, and content 150, interconnected by
network 120. In this embodiment, components of cognitive computing
environment 100 (e.g., storage 110, analytic engine 130, etc.) are
each a part of separate computing systems connected by network 120.
In another embodiment, a portion of such components may be included
in one or more computing systems, such that any separate components
are configured to exchange data between one another over network
120. Furthermore, such computing systems may be desktop computers,
laptop computers, specialized computer servers, or any other
computer system known in the art. In certain embodiments, the
computing systems may represent computer systems utilizing
clustered computers and components to act as a single pool of
seamless resources when accessed through network 120. For example,
such embodiments may be used in data center, cloud computing,
storage area network (SAN), wide area network (WAN), and network
attached storage (NAS) applications. In certain embodiments, the
computing systems represent virtual machines. In general, the
computer systems described herein are representative of any
electronic device, or combination of electronic devices, capable of
executing machine-readable program instructions, in accordance with
an embodiment of the present invention, as described in greater
detail with regard to FIG. 4. In this embodiment, the computing
systems are implemented in various cloud computing environments, as
described in greater detail with regard to FIGS. 5 and 6.
[0014] Network 120 can be, for example, a local area network (LAN),
a wide area network (WAN) such as the Internet, or a combination of
the two, and include wired, wireless, or fiber optic connections.
In general, network 120 can be any combination of connections and
protocols that will support communications between storage 110,
analytic engine 130, application 140, and content 150, in
accordance with an embodiment of the invention.
[0015] Storage 110 represents a storage component configured to
store a corpus generated from content 150 in addition to enrichment
analysis data (e.g., annotations and metadata) produced by analytic
engine 130, as described in greater detail with regard to FIG. 2.
In this embodiment, storage 110 includes a relational database,
such as IBM.RTM. DB2.RTM., or another type of relational database
management system (RDBMS), storing the normalized collection of the
enrichment analysis data. IBM.RTM. DB2.RTM. are registered
trademarks of International Business Machines Corporation. Storage
110 leverages the benefit of a relational database by providing
features and functions such as, query optimization, filtering,
summarization, joins, groupings, derived values, incremental
ingestions, updates/deletes, security, reliability, performance,
scalability, disaster recovery, and auditability. These features
and functions enable cognitive computing environment 100 to manage
content 150 at the content provider, document, annotator, and
annotation level, and further provides the ability to participate
in batch and incremental ingestion, as well as provide versioning
capabilities. Furthermore, enrichment analysis data stored in
storage 110 may be used to populate an annotation store from which
knowledge graphs, searchable indices, and graph databases can be
generated, as described in FIG. 2.
[0016] Analytic engine 130 is a software component configured to
process and ingest content 150 into a searchable corpus as well as
generate enrichment analysis data for the corpus, including
annotations and metadata. For example, analytic engine 130 may
validate structured, semi-structured, and unstructured data
included in content 150 to ensure that it is readable, searchable,
and comprehensible. Furthermore, analytic engine 130 provides
configurable, extensible ingestion workflows to support natural
language processing, text analysis, text mining and content
enrichment capabilities. Features such as language identification,
semantic scoring, lemmatization, clustering and classification, key
phrase extraction, synonym expansion, document and entity sentiment
calculation, dictionary-based and/or statistical-based entity
extraction, predictive analytics, and trending entity
identification may be supported by analytic engine 130. In this
embodiment, analytic engine 130 relies on rule-based and machine
learning to analyze, process and ingest content 150. For example,
document and entity-sentiment calculation may be performed by
analytic engine 130 using machine learning, and predictive
analytics may be performed by analytic engine 130 using
classification regression and pairwise correlations (i.e.,
rule-based).
[0017] In this embodiment, analytic engine 130 leverages the
above-mentioned features and capabilities to generate annotations
and metadata for an entire corpus and then populate an annotation
store with the generated annotations and metadata. For example,
analytic engine 130 may prepare (e.g., evaluate, validate, curate,
transform and/or enhance) the data included in content 150.
Afterwards, analytic engine 130 may produce a collection of vetted
data (e.g., ground truth or gold standard data) by involving
subject matter experts to create resources (e.g., annotation
guidelines, types of systems, dictionaries of terms). Additionally,
the collection of vetted data can be produced by pre-annotating the
data based on dictionaries provided to analytic engine 130, where
pre-annotating is a process of machine-annotating the data of
content 150 to the extent possible before a machine-learning model
is available to do so. Pre-annotation can reduce human-annotation
labor by replacing some human annotation creation with mere
verification of the correctness of machine annotation. In this
example, after establishing the collection of vetted data, analytic
engine 130 may be used to train an algorithm for automatically
adding annotations to large collections of documents, such as
collections that include millions of documents. Accordingly,
analytic engine 130 uses the trained algorithm as well as the
above-mentioned features (e.g., language identification, semantic
scoring, lemmatization, clustering and classification, key phrase
extraction, synonym expansion, document and entity sentiment
calculation, dictionary-based and/or statistical-based entity
extraction, predictive analytics, and trending entity
identification) to create annotations for data in content 150.
[0018] Content 150 represents structured, semi-structured, and
unstructured data that is provided by one or more content providers
and is used to create a searchable corpus for analytic engine 130
to later analyze. In one embodiment, data included in content 150
may be evaluated and then later curated, enhanced, and/or
transformed by analytic engine 130 prior to ingesting the data into
a searchable corpus. For example, content 150 may be evaluated to
ensure that text-based resources such as journal articles,
textbooks, and research documents are annotated with headings or
tags to assist cognitive computing environment 100 identify and
classify the content in specific documents.
[0019] Application 140 represents a software component configured
to analyze a corpus and enrichment analysis data (e.g., annotations
and metadata) to perform higher level cognitive processing, such as
knowledge and insight analytics relative to a domain, a topic, a
person, or an issue. Application 140 interacts with storage 110 to
retrieve pertinent information that is necessary to answer complex
queries and summarize annotations across many millions of documents
included in the corpus. In one embodiment, application 140 may be
used in conjunction with analytic engine 130 to generate and/or
regenerate cognitive computing artifacts such as knowledge graphs,
searchable indices, and graph databases, as described in FIG.
2.
[0020] The descriptions of the various embodiments of the present
invention have been presented for the purposes of illustration, but
are not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing form the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
[0021] FIG. 2 is a block diagram of storage 110, in accordance with
an embodiment of the present invention. In this embodiment,
annotation store 112 is a relational database, such as IBM.RTM.
DB2.RTM., from which the runtime artifacts including knowledge
graph 113, searchable index 114 and graph database 115 are
generated. In another embodiment, other higher-level knowledge
artifacts may be generated, such as analysis and scoring
algorithms. Annotation store 112 provides the ability for
incremental updates, deletes and versioning for enrichment analysis
data because it is a relational database. In contrast, typical
cognitive computing systems do not implement such relational
databases, thereby requiring a full replacement when an annotator
changes. The use of a relational database in storage 110 provides
for more efficient filtering operations. For example, consider a
corpus with many generated annotations that may not be needed for a
particular use case. In this example, the relational database, or
annotation store 112, allows for efficient creation of an instance
that only includes annotations needed for the particular use case,
and eliminates the remaining generated annotations.
[0022] Storage 110 relies on a combination of annotation store 112
as well as a stored corpus including the full textual content of
each document, thus eliminating the need for UIMA.TM. CAS objects,
JSON objects, and/or other structures for each document.
Furthermore, such objects and/or structures may be discarded when
the annotators are executed and annotation store 112 is populated,
thus reducing the storage requirement and save time during
deployment. Additionally, annotation store 112 is not only a
representation of the annotators which have been executed, but it
may also become an additional source of correlations and insights
that can provide for new annotations, which may also be populated
back into annotation store 112. Accordingly, such a recursive
annotation process may be advantageous, because a first analysis is
performed with individual annotators, and then another analysis may
be performed against the entire corpus.
[0023] Knowledge graph 113 represents a knowledge base used to
enhance searchability with semantic-search information gathered
from a variety of sources. For example, application 140 may
interact with knowledge graph 113 to resolve a query without
navigating through individual documents of the corpus and
subsequently assemble the pertinent information. Searchable index
114 represents a collection of data, such as a table or index that
is used to achieve efficient and fast search responses because,
instead of application 140 searching the text directly, application
140 searches an index instead. Graph database 115 represents a
scalable database that uses graph structures for semantic queries
with nodes, edges and proprieties to represent and store data.
Furthermore, the graph, or edge or relationship, directly relates
data items in the store, allowing data in the store to be linked
together directly, and retrieved with one operation. Accordingly,
the enrichment analysis data stored in storage 110, including the
annotations and metadata which identify the meaning of text, and
not just text patterns, in addition to the text content of a corpus
provides the ability to perform annotation search and analysis in a
comprehensive and corpus-scoped manner.
[0024] FIG. 3 is a flowchart illustrating operational steps for
creating and storing enrichment analysis data, in accordance with
an embodiment of the present invention. In this embodiment, content
150 includes various types of data used to generate a corpus.
Furthermore, analytic engine 130 provides tools and services to
analyze, process and ingest content 150 resulting in the generation
of enrichment analysis data that is stored in storage 110.
[0025] Analytic engine 130 prepares the data (step 302). In one
embodiment, analytic engine 130 may evaluate, validate, curate,
transform and/or enhance the data included in content 150 prior to
ingestion. Furthermore, analytic engine 130 may rely on rule-based
and/or machine learning techniques to ensure that all relevant
terminology, such as entity names, domain vocabulary, and special
expressions, are identifiable prior to ingestion.
[0026] After the data is prepared, analytic engine 130 creates
annotations and metadata for the documents included in the
generated corpus (step 304). Metadata may include details about the
generated annotations or other word, phrase, concept, and/or
document relationships. As previously described, analytic engine
130 uses trained algorithms, rule-based, and other machine-learning
techniques to create annotations for data in the generated corpus.
For example, a collection of vetted data (e.g., ground truth or
gold standard data) may be created to train an algorithm used by
analytic engine 130. In this example, the use of the trained
algorithm in addition to other analytic engine 130 features (e.g.,
language identification, semantic scoring, lemmatization,
clustering and classification, key phrase extraction, synonym
expansion, document and entity sentiment calculation,
dictionary-based and/or statistical-based entity extraction,
predictive analytics, and trending entity identification) enable
analytic engine 130 to create annotations and metadata for the
documents included in the generated corpus. Furthermore, analytic
engine 130 may support incremental annotation, as well as support
incremental ingestion. In instances where corpus updates are
received, analytic engine 130 may also analyze and process the data
included in the corpus updates to create, generate or regenerate,
annotators and metadata.
[0027] Analytic engine 130 subsequently processes and enriches the
annotations and metadata to generate enrichment analysis data (step
306). For example, as previously described, annotation store 112 is
not only a representation of the annotators which have been
executed, but it may also become an additional source of
correlations and insights that can provide for new annotations,
which may also be populated back into annotation store 112.
Accordingly, such a recursive annotation process performed during
this enrichment phase may be advantageous, because a first analysis
is performed with individual annotators, and then another analysis
may be performed against the entire corpus. In another example,
enriching the annotations and metadata may performed after
receiving an updated collection of vetted data (e.g., ground truth
or gold standard data) due to, for example, an updated corpus. In
this example, if an updated collection of vetted data is used to
re-train an algorithm of analytic engine 130, then analytic engine
130 applies the newly trained algorithm along with any updated
analysis features to generate new annotations and metadata.
Accordingly, the new annotations and metadata results are compared
to the previously generated annotations and metadata, and any
identified conflicts are resolved. Adjudication in this phase is
needed to ensure accurate and consistently annotated documents
included in the enrichment analysis data are promoted to ground
truth data. In one embodiment, annotator updates may be received
during enrichment, whereby an incremental annotation process may be
initiated.
[0028] Once the enrichment analysis data is generated, then it is
stored in storage 112 (step 308). As previously described,
annotations and metadata that are part of the enrichment analysis
data may be stored in a relational database, such as annotation
store 112, thereby providing application 140 with the ability to
search and analyze annotations at a corpus-scoped manner. Once
enrichment analysis data is stored in a relational database,
cognitive computing environment 100 is configured to receive a
corpus-scoped query, optimize the corpus-scoped query using query
optimizations features of a relational database management system,
and resolve the corpus-scoped query using the enrichment analysis
data that described annotations and metadata for the entire corpus.
The use of relational databases enables for the generation of
additional runtime artifacts, such as knowledge graphs, searchable
indices, and graph databases, which may be used by analytic engine
130 to resolve the corpus-scoped query.
[0029] FIG. 4 is a block diagram of internal and external
components of a computer system 400, which is representative the
computer systems of FIG. 1, in accordance with an embodiment of the
present invention. It should be appreciated that FIG. 4 provides
only an illustration of one implementation and does not imply any
limitations with regard to the environments in which different
embodiments may be implemented. In general, the components
illustrated in FIG. 4 are representative of any electronic device
capable of executing machine-readable program instructions.
Examples of computer systems, environments, and/or configurations
that may be represented by the components illustrated in FIG. 4
include, but are not limited to, personal computer systems, server
computer systems, thin clients, thick clients, laptop computer
systems, tablet computer systems, cellular telephones (e.g., smart
phones), multiprocessor systems, microprocessor-based systems,
network PCs, minicomputer systems, mainframe computer systems, and
distributed cognitive computing environments that include any of
the above systems or devices.
[0030] Computer system 400 includes communications fabric 402,
which provides for communications between one or more processors
404, memory 406, persistent storage 408, communications unit 412,
and one or more input/output (I/O) interfaces 414. Communications
fabric 402 can be implemented with any architecture designed for
passing data and/or control information between processors (such as
microprocessors, communications and network processors, etc.),
system memory, peripheral devices, and any other hardware
components within a system. For example, communications fabric 402
can be implemented with one or more buses.
[0031] Memory 406 and persistent storage 408 are computer-readable
storage media. In this embodiment, memory 406 includes random
access memory (RAM) 416 and cache memory 418. In general, memory
406 can include any suitable volatile or non-volatile
computer-readable storage media. Software is stored in persistent
storage 408 for execution and/or access by one or more of the
respective processors 404 via one or more memories of memory
406.
[0032] Persistent storage 408 may include, for example, a plurality
of magnetic hard disk drives. Alternatively, or in addition to
magnetic hard disk drives, persistent storage 408 can include one
or more solid state hard drives, semiconductor storage devices,
read-only memories (ROM), erasable programmable read-only memories
(EPROM), flash memories, or any other computer-readable storage
media that is capable of storing program instructions or digital
information.
[0033] The media used by persistent storage 408 can also be
removable. For example, a removable hard drive can be used for
persistent storage 408. Other examples include optical and magnetic
disks, thumb drives, and smart cards that are inserted into a drive
for transfer onto another computer-readable storage medium that is
also part of persistent storage 408.
[0034] Communications unit 412 provides for communications with
other computer systems or devices via a network (e.g., network
120). In this exemplary embodiment, communications unit 412
includes network adapters or interfaces such as a TCP/IP adapter
cards, wireless Wi-Fi interface cards, or 3G or 4G wireless
interface cards or other wired or wireless communication links. The
network can comprise, for example, copper wires, optical fibers,
wireless transmission, routers, firewalls, switches, gateway
computers and/or edge servers. Software and data used to practice
embodiments of the present invention can be downloaded through
communications unit 412 (e.g., via the Internet, a local area
network or other wide area network). From communications unit 412,
the software and data can be loaded onto persistent storage
408.
[0035] One or more I/O interfaces 414 allow for input and output of
data with other devices that may be connected to computer system
400. For example, I/O interface 414 can provide a connection to one
or more external devices 420, such as a keyboard, computer mouse,
touch screen, virtual keyboard, touch pad, pointing device, or
other human interface devices. External devices 420 can also
include portable computer-readable storage media such as, for
example, thumb drives, portable optical or magnetic disks, and
memory cards. I/O interface 414 also connects to display 422.
[0036] Display 422 provides a mechanism to display data to a user
and can be, for example, a computer monitor. Display 422 can also
be an incorporated display and may function as a touch screen, such
as a built-in display of a tablet computer.
[0037] Referring now to FIG. 5, illustrative cognitive computing
environment 50 is depicted. As shown, cognitive computing
environment 50 comprises one or more cloud computing nodes 10 with
which local computing devices used by cloud consumers, such as, for
example, personal digital assistant (PDA) or cellular telephone
54A, desktop computer 54B, laptop computer 54C, and/or automobile
computer system 54N may communicate. Nodes 10 may communicate with
one another. They may be grouped (not shown) physically or
virtually, in one or more networks, such as Private, Community,
Public, or Hybrid clouds as described hereinabove, or a combination
thereof. This allows cognitive computing environment 50 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. The types of computing devices 54A-N shown in
FIG. 4 are intended to be illustrative only and that cloud
computing nodes 10 and cognitive computing environment 50 can
communicate with any type of computerized device over any type of
network and/or network addressable connection (e.g., using a web
browser).
[0038] Referring now to FIG. 6, a set of functional abstraction
layers provided by cognitive computing environment 50 (FIG. 5) is
shown. The components, layers, and functions shown in FIG. 6 are
intended to be illustrative only and embodiments of the invention
are not limited thereto. As depicted, the following layers and
corresponding functions are provided:
[0039] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0040] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0041] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cognitive computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cognitive computing environment,
and billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cognitive
computing environment for consumers and system administrators.
Service level management 84 provides cloud computing resource
allocation and management such that required service levels are
met. Service Level Agreement (SLA) planning and fulfillment 85
provide pre-arrangement for, and procurement of, cloud computing
resources for which a future requirement is anticipated in
accordance with an SLA.
[0042] Workloads layer 90 provides examples of functionality for
which the cognitive computing environment may be utilized. Examples
of workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and
cognitive computing environment 96.
[0043] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0044] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0045] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0046] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0047] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0048] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0049] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0050] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0051] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cognitive
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0052] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0053] Characteristics are as follows:
[0054] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0055] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0056] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0057] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0058] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0059] Service Models are as follows:
[0060] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0061] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0062] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0063] Deployment Models are as follows:
[0064] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0065] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0066] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0067] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds). A cognitive computing environment is service oriented with
a focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0068] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the invention. The terminology used herein was chosen
to best explain the principles of the embodiment, the practical
application or technical improvement over technologies found in the
marketplace, or to enable others of ordinary skill in the art to
understand the embodiments disclosed herein.
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