Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Autonomic Computing Architecture by self defined URIs
1. UNIVERSITY OF SCIENCE AND TECHNOLOGY
COLLEGE OF GRADUATE STUDIES AND ACADEMIC
ADVANCEMENT
Faculty of Computer Science and Information Technology
Autonomic Computing Architecture by Self-defined URIS
by
Abdelhafiz Ahmad Khoudour
Supervisor
Dr. Nadir Kamal Idriss
January 2017
2. Outline
- Autonomic Architecture
- Autonomic computing
- Research Objective
- Research Problems
- Related work (literature review)
- Semantic technology tools
- Case study
- Implementation
- Result and Research contribution
4. Objectives
1. To realize self-management system by providing self-defined.
2. Implement the using of RDF, OWL, and SPARQL.
3. Reduce the cost and complexity
4. Allow administrators to specify high-level policies
5. Let the system manage itself
5. Problem Statement
Autonomic architecture is consist of four main components, three of them are semantic tools technology
(RDF, OWL and SPARQL), in addition, the autonomic manager, the research problem statement is how to
realize self-management by providing self-defined through the architecture.
The recent problem
To realize the self-management by providing self-defined.
Proposed solution
Using Ahmed Gasim hospital URI (http://www.csrtahmedgasim.com) .
Case study
Coronary Heart Disease
6. Autonomic Computing
An autonomic application/system is a collection of autonomic elements, which implement intelligent
control loops to monitor, analyze, plan, and execute using knowledge of the environment.
Autonomic Architecture
Multi-agent systems
A single self-managing component can be built using intelligent agents and every agent has its own
goals, which drive its decisions.
Architecture design-based
Individual components are not for every autonomic.
7. Characteristics
The four primary are:
Self-configuring
System adapt to change environment such as (plug and play devices, setup wizard…etc)
Self-healing
System discover, diagnose and react to disruption.
Self-optimizing
It requires hardware and software to monitor, tune and maximize resource utilization efficiency.
Self-protecting
System protect, anticipate , detect, identify and protect against any attacks from anywhere against
unauthorized access.
8. Autonomic elements (AEs)
Are the basic block and their interaction produce the self-management.
Autonomic Element Parts
• Managed Element (ME)
Is a system component it can be hardware, application software or an entire system.
Autonomic Manager (AM)
Is a component that implements an intelligent control loop, has four parts
Monitor, analyze, plan and execute.
9. Policies
A set of administrator ideas (knowledge) stored to guide AM.
Knowledge
is the shared data among AM parts such as (metrics, symptoms, policies)
11. AC level
Basic level
Each system element perform manually by IT professionals.
Managed level
System management technologies can use to collect information from different systems. It helps
administrator to collect and analyze information it is starting point of automation of IT task.
Predictive level
Individual components monitor themselves, analyze changes, and offer advices, reducing and
improve decision making depend to the person.
Adaptive level
IT components can individually and group monitor, analyze operations, offer advise with
minimal human intervention
Autonomic level
Business policy derives overall IT management.
12. Challenges
1. Conceptual challenges
Defining, controlling and implementing behaviors
Control dynamic and multi agent system
Providing effective model for negotiation
Deigning statistical models to detect or predict overall problem
2. Architecture Challenges
The self-managing behavior of constituent element and their interaction.
13. 3. Application Challenges
The formulation and development of systems and application that are capable of managing
themselves(framework, programming model…etc.)
4. Middleware Challenges
Require realizing autonomic behavior in a robust, reliable and scalable manner, in spite dynamism
and uncertainty of the system.
14. Reference no Problem/challenge Solution Characteristic Remark
[4]
A complete survey of autonomic computing
systems and their importance
Provide detailed information about autonomic
computing concept
[8] spending long hours for short distances in traffic
using a sensor and a sensor node connecting
with an automatic Wireless Sensor Networks
(self-configuring, self- optimization and
self-healing)
[9]
present and analyze autonomic characteristics of
Microsoft SQL Server, in addition, how self-
managing behavior of an ADBMS can reduce the
workload of a DBA adding to that which
components are not fully autonomic
Using self-* characteristics of Microsoft SQL
Server components, and algorithms to reduce
human intervention.
self-Optimization, self-configuration, Self-
Healing, Self-protection, Self-Inspection,
Self- Organization
[10]
To describe the architecture of stable autonomic
systems which consist of a main characteristics of
autonomic systems (Self-healing, Self-optimizing,
Self-protecting and Self-configuring)
A generic architecture of autonomic approach r
contains two entities. Autonomic Element,
comprises managed resources and distributes
services to humans or other autonomic
elements, sensors, effectors, and five-
component (Monitor Analyzer, Plan, Execute,
and shared knowledge). And Autonomic
Manager, to preserve correct software
architecture and autonomic control loop(collect,
analyze, decide, and act)
Self-healing, Self-optimizing, Self-
protecting and Self-configuring
Architecture designed
based
[11]
the use of an architectural approach to self-
management, that to provide a systems are
scalable, support dynamic composition , rigorous
analysis , flexible and robust in the presence of
change
A self-managed software architecture
a three layer reference model proposed
self-management
Architecture designed
based
[12]
To describe an architectural approach to explore
the use of autonomic systems for data center
management and resource allocation.
Creating two prototype autonomic systems
describe the external interfaces and behaviors to
make an individual component autonomic, in
addition, how to compose systems out of these
autonomic components in such a way that the
system as a whole is self-managing.
self-managing components
Architecture designed
based
Related work
15. Semantic technology tools
It consist three components :
1- Resource description framework (RDF)
2- Ontology web language (OWL)
3- SPARQL
16. Resource Description framework(RDF)
Is a framework for representing information as a graph in the web .
RDF Triples
Contains three components:
- The subject : URIS or a blank node.
- The predicate : URIS
- The Object :URIS, a literal or a blank node.
17. RDF Serialization Formats
1-RDF/XML format
2- Notation 3
3- Turtle
4- N-Triples
<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:dc="http://purl.org/dc/elements/1.1/">
<rdf:Description rdf:about = "URI of the statement’s subject/">
<predicateURI rdf:resource="URI of the statement’s object/">
</rdf:Description>
</rdf:RDF>
RDF syntax
18. rdf : RDF (RDF/XML document)
rdf : about (attribute specifies the resource you want to define )
rdf : description ( encapsulate the properties of a specific resource )
rdf : resource ( an attribute define a resource that identifies a Property in a RDF triplets).
rdf : data type (defining data type of the resource)
rdf : ID ( defining ID of the resource)
RDF Attributes
24. RDF Schemas
Core classes
rdfs : Resource (the class of all resources)
rdfs : Class ( the class of all classes)
rdfs : Literal ( the class of all literals (strings))
rdfs : property ( the class of all property)
rdfs : statement ( the class of all reified statement)
Core property for defining relationship
rdfs : type
rdfs : subclassOf
Restricting properties
rdfs : domain
rdfs : range
25. Ontology web language (OWL)
To combine entities (classes, property) to create new entities such as :
• Expression
• Rule
• Taxonomy (classification)
• Thesaurus (taxonomy extension)
• Axiom (reasoning with RDF and RDF schema)
27. OWL description
- Class
- Subclass
- Property (Object property – Data property)
- -- Domain
- -- Range
- Individuals
28. - Ontology Reasoning
• Check the consistency of the ontology and the knowledge
• Check for unintended relationships between classes,
• Automatically classify instances in classes.
- Annotation
Annotate with useful information such as labels, comments, authors and creation date, it simply
associates property value pairs to ontology entities.
29. SPARQL
To pull data from a growing collection of public and private data.
Basic graph pattern
Prefixed name : is equivalent to XML namespace, instead of using URL more, one can use prefix .
Prefix IRI
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
xsd: http://www.w3.org/2001/XMLSchema#
dc: http://purl.org/dc/elements/1.1
30. Triple pattern
Is writing as subject, predicate and object terminate with a full stop .
Synatax for literals
xsd:integer;
"chat "
True, false
Syntax for query variable
?x , ?y
$x, $y
Syntax for blank nodes
[ ]
“_:abc "
31. Graph pattern
Consist of two triple patterns
Query forms
-Construct
-Select
-Describe
-Ask
SPARQL Query result format
- JSON Format
- CSV and TSV Format
32. Self-defined
URIs (Uniform Resource Identifier) are the standard mechanism for identifying resources
on the Web. It fits well into the Semantic Web for the following two main reasons:
1. It provides a mechanism to uniquely identifying a given resource.
2. It specifies a uniform way to retrieve machine-readable descriptions about the resource
being identify by the URI.
Another benefit of using URIs to represent subject and object resources is relate to their
global uniqueness. In this research, http://csrtahmedgasim.com/ only will create any new
URI that guarantees the global uniqueness of URIs and certainly prevents name clashes.
Example : http://csrtahmedgasim.com/#Patient.
33.
34.
35. Case study
Coronary heart disease
Causes
• Smoking
• High levels of certain fats and cholesterol in the blood
• High blood pressure
• High levels of sugar in the blood due to insulin resistance or diabetes
• Blood vessel inflammation
Symptoms
• Chest pain (angina)
• Shortness of breath
• Heart attack
36. Diagnosis
• ECG (Electrocardiogram)
• Echocardiography
• Chest X Ray
• Blood Tests
• Coronary Angiography and Cardiac Catheterization
Treatments
• Drug
• Surgery
• Prevention
37. Ahmed Gasim Hospital
Khartoum-sudan
Objectives
1. Providing modern and advanced medical services to patients with heart and kidney in Sudan to
cover the diagnostic needs and others complementary services.
2. Promote scientific research and take care of internal and external training.
3. Efficiency of medical staff escort lay evolution of global progress in this area.
4. The development of medical devices
38. Units and Department
1. Computer and information technology
2. Cather unit
3. Cardio intensive unit
4. Cardio surgery unit
5. Cardio pediatric unit
6. Blood blank
7. Excellence unit
8. Pharmacia and medical logistic unit
9. Kidney and renal unit
10. Medical nutrition unit
11. Statistical unit
12. Protonial dialysis unit
13. Kidney and renal transplantation department
14. Lab department
39. REC NO
PATIENT
NAME
AGE
ADMISSION
DATE
DISCHARGE
D
CAUSES SYMPTOMS
TEST
S
DIAGNOSES
TREATMENT
MEDICATION SURGERY
1
TAHA
HUSEIN
57 24/05/2016 13/6/2016
HEART
FAILURE
SHORTNESS
OF BREATH
PRODUCTIVE
CAUGHT
-
ECHO
CORONARY
ANGIOGRAPHY
LASIX
ALDACTONE
LISINOPRIL
BISOPROLOL
ASPRINE
ATORVA
2
MOHYADI
NE OMER
1YEAR
+2M
18/2/2016 HEREDITY - B.T
CEFAZOLINE
LASIX
PARACETAMOL
ALDACTONE
CAPTOPRIL
SURGERY
3
MAZAHIR
ATEIB
55 1/6/2016 5/6/2016
HYPERTENSIV
E
Diabetes
PALPITATION
FEVER
ECHO
CLAXIN
WARFERIN
DIGOXIN
4
HAWA
MOHAME
D
70 25/4/2016 1/6/2016 AGE
LIMP
SWELLING
SHORTNESS
OF BREATH
CAUGHT
PALPITATION
B.T ECG
ECHO
PANTOPRAZOL
LASIX
ALDACTONE
ZINOPRIL
BISOPROLOL
ATORVA
ASPIRINE
5
AMANA
BILAL
45 10/6/2016 16/6/2016
HYPERTENSIV
E
LIMP
SWELLING
SHORTNESS
OF BREATH
ECG
ECHO
AMILODOBIN
DIGOXIN
ALDACTONE
LASIX
ASPRINE
BISPROLOL
WARFARIN
44. PREFIX is SPARQL equivalent of XML namespaces. So instead of using whole URL
again and again one can use prefix.
SELECT keyword is used to select the data items that the query will return. This
is optional in this case. If we don’t mention it, query will be run against the
current file.
WHERE clause is used to specify the triple/graph pattern that query matches
against a RDF graph. WHERE keyword itself is optional. A general form of this
clause will be
WHERE { ?subject ?predicate ?object }
45. Queries
Input
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT *
WHERE
{ { ?class a owl:Class }
?class a ?classType }
Output
This query declares a class and its type by saying that it has an owl: Class.
46. Input
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?subject ?object
WHERE { ?subject rdfs:subClassOf ?object }
Output
This query ask about two variables of subject and object, in condition of the subject to be subclass.
47. Input
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT *
WHERE {
?s ?p ?o . }
Output
This query actually just asks for all the triples in the default graph.
48. Input
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT distinct ?ind ?class
WHERE { ?ind rdf:type ?class }
ORDER BY ?class
Output
This query shows that individuals and their classes.
52. Result
-Autonomic architecture components
-The open source application (Protégé)
Evaluation
The Autonomic level is the Managed level.
The system is flexible and robust in presence of change
The reuse of the knowledge-based.
53. Research contribution
1. Improve the hospital’s ability to use patient data for generating new knowledge.
2. Improve the future patient care through outcomes (Reports).
3. Build a repository collects and stores various data.
4. Help doctors, users, and patients to providing ad hoc query to data published in the web from
anywhere.
5. Help the clinical research center to collect data.
