Semantic Interoperability in Infocosm:   Beyond Infrastructural and Data Interoperability in Federated Information Systems...
<ul><li>Information Integration Perspective: Distribution, Heterogeneity, Autonomy </li></ul>Three perspectives <ul><li>In...
Evolving targets and approaches in integrating data and information:  a personal perspective Infocosm Generation 1 Generat...
Generation I <ul><li>Data recognized as corporate resource -- leverage it! </li></ul><ul><li>Most data in structured datab...
Generation II <ul><li>Significant improvements in computing and connectivity (standardization of protocol, public network,...
<ul><li>Query only, little attention to updates; extensive use of IR techniques </li></ul><ul><li>Focus shift from data to...
<ul><li>Increasing information overload </li></ul><ul><li>Changes in Web architecture: push,… </li></ul><ul><li>Broader va...
<ul><li>Broader variety of users and applications; well beyond business and scientific uses (e.g., focused marketing-- mor...
Generation I and Lessons from the Federated Database Systems Research
Dimensions for interoperability and integration:  Perspective used for Federated Databases Distribution Autonomy Heterogen...
FDBS: Schema Architecture <ul><li>Model Heterogeneity:   Common/Canonical Data Model   Schema Translation </li></ul><ul><l...
Heterogeneity in FDBMSs <ul><li>Hardware/System </li></ul><ul><li>instruction set </li></ul><ul><li>data representation/co...
Characterization of Schematic Conflicts in Multidatabase Systems Schematic Conflicts Domain Definition Incompatibility Nam...
Observations and Lessons Learnt <ul><li>“ tightly coupled”  vs  “loosely coupled” debate </li></ul><ul><li>“ good common d...
Retracing the path  without learning from past expeditions <ul><li>Steps for transitioning from Data Marts to Warehouses: ...
 
Generation 1 concern: So far (schematically),  yet so near (semantically)! Generation 3 concern: So near (schematically), ...
Generation II and Generation III
Information Brokering:  A Three-Level Approach Ontology Content Representation used-by abstracted-into Semantic (Domain, A...
An Architecture  for Information Brokering Information System 1 Information System N INFORMATION BROKERING Data Brokering ...
Generation 2: Limited Types of Metadata, Extractors, Mappers, Wrappers
Global/Enterprise Web Repositories Generation 2 Nexis UPI AP DB METADATA EXTRACTORS
Junglee Gen.2 Data Integration Data Publishing Publishing  Rule Publisher Extraction  Rules Extractor Mapping  Rules Mappe...
Find Marketing Manager positions in a company that is within 15 miles of San Francisco and whose stock price has been grow...
<ul><li>can automatically identify data/media type </li></ul><ul><li>can be extended at any time (pre-specified  or  param...
A Classification  of Metadata <ul><li>Content Independent Metadata   e.g. creation-date, location, ... </li></ul><ul><li>C...
Query Processing and Information Requests <ul><li>traditional queries based on keywords </li></ul><ul><li>attribute-based ...
VisualHarness . . Image Data Color Comp Texture Structure Other Attributes VIR Extraction Null Image Metadata for combined...
Metadata Brokering in VisualHarness
VisualHarness    An Example
What else can Information Brokering do?
<ul><li>WWW </li></ul><ul><li>A confusing heterogeneity of media, formats (Tower of Babel) </li></ul><ul><li>Information c...
Ontologies for  semantic interchange <ul><li>Need for “ transcending ” local subject areas/domains  => Design  Adaptable  ...
The InfoQuilt Project http://lsdis.cs.uga.edu/infoquilt
Correlating Data on the Web today <ul><li><TITLE> A Scenic Sunset at Lake Tahoe </TITLE> </li></ul><ul><li><p> </li></ul><...
MREF Metadata Reference Link -- complementing HREF Creating “logical web” through  Media Independent Metadata based Correl...
Metadata Reference  Link (<A MREF …>) <ul><li><A HREF=“URL”> Document Description </A> </li></ul><ul><li>physical link bet...
  Correlation based on  Content-descriptive Metadata  Some interesting <A MREF  KEYWORDS=“scenic waterfall mountain”; THRE...
Correlation based on  Content-based Metadata height, width and size Some interesting  <A MREF  KEYWORDS= “scenic waterfall...
Metadata, Domain Specific Ontologies Get the  titles ,  authors ,  documents , maps  published by the United  States Geolo...
TIGER/Line DB Population:  Area: Boundaries : Land cover: Relief: Census DB Image/Map DB Regions (SQL) Boundaries  Image F...
Domain Specific Correlation <ul><li>Potential locations for a future shopping mall identified by all regions having a popu...
 
 
InfoQuilt Architecture (partial) Media Independent Information Requests [Browsing Collections, Keyword-based queries, Attr...
What next  (after comprehensive use of metadata)  ? <ul><li>Context, context, context </li></ul><ul><li>Semantic Proximity...
A Semantic Taxonomy Semantic  Proximity Semantic Resemblance Semantic Relevance Semantic Relationship Semantic Equivalence...
Tools to support semantics  ontologies profiles context domain-specific metadata
Computing Communication Information Knowledge Data Decision Connectivity and Data Access Interoperability Cooperation
Computing Communication Information Knowledge Data Decision Connectivity Interoperability Cooperation Interoperability in ...
Computing Communication Information Knowledge Data Decision Connectivity Interoperability Cooperation Interoperability in ...
Computing Communication Information Knowledge Data Connectivity Interoperability Cooperation Where we are headed Semantic ...
Cognition Heuristics Learning Semantics KNOWLEDGE Introspection Deduction
Cooperative Information Systems Collective exploitation of complementary technologies Information Management Coordination ...
Computing Communication Information Interoperablity Knowledge Data Infocosm Cooperating Information Systems
Summary  <ul><li>We have addressed many data level (schematic, representational,…) issues so far </li></ul><ul><li>We are ...
Agenda for Research <ul><li>Interoperation not at systems level, but at informational and possibly knowledge level </li></...
http://lsdis.cs.uga.edu [See publications on Metadata, Semantics, InfoHarness/InfoQuilt] [email_address]
Upcoming SlideShare
Loading in …5
×

Semantic Interoperability in Infocosm: Beyond Infrastructural and Data Interoperability in Federated Information Systems

745 views

Published on

Amit Sheth, Keynote: International Conference on Interoperating Geographic Systems (Interop’97), Santa Barbara, December 3-4 1997 .

Related technical paper: http://knoesis.org/library/resource.php?id=00230

Published in: Education
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
745
On SlideShare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
9
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

Semantic Interoperability in Infocosm: Beyond Infrastructural and Data Interoperability in Federated Information Systems

  1. 1. Semantic Interoperability in Infocosm: Beyond Infrastructural and Data Interoperability in Federated Information Systems Keynote Talk International Conference on Interoperating Geographic Systems (Interop’97), Santa Barbara, December 3-4 1997 Amit Sheth Large Scale Distributed Information Systems Lab University of Georgia http://lsdis.cs.uga.edu Thanks: Vipul Kashyap, Kshitij Shah
  2. 2. <ul><li>Information Integration Perspective: Distribution, Heterogeneity, Autonomy </li></ul>Three perspectives <ul><li>Information Brokering Perspective: Data, Metadata, Semantic (Terminological, Contextual) </li></ul><ul><li>“ Vision” Perspective: Connectivity+Computation, Information, Knowledge </li></ul>
  3. 3. Evolving targets and approaches in integrating data and information: a personal perspective Infocosm Generation 1 Generation 2 Generation 3 Mermaid DDTS Multibase, MRDSM, ADDS, IISS, Omnibase, ... Early 80s Infoscopes, HERMES, SIMS, ... TSIMMIS,Harvest, RUFUS,... VisualHarness InfoHarness 1990 Digital Library Projects, .. InfoQuilt 1997
  4. 4. Generation I <ul><li>Data recognized as corporate resource -- leverage it! </li></ul><ul><li>Most data in structured databases (and the rest in files), different data models, transitioning from Network and Hierarchical to Relational DBMSs </li></ul><ul><li>Connectivity/access -- a major issue </li></ul><ul><li>Heterogeneity (system, modeling and schematic) as well as need to support autonomy posed main challenges </li></ul><ul><li>Support for corporate IS applications as the primary objective, update often required, data integrity important </li></ul>
  5. 5. Generation II <ul><li>Significant improvements in computing and connectivity (standardization of protocol, public network, Internet/Web); remote data access as given </li></ul><ul><li>Increasing diversity in data formats, with focus on variety of textual data and semi-structured documents (and lesser focus on structured data) </li></ul><ul><li>Many more data sources, diverse domains, but not necessarily better understanding of data </li></ul><ul><li>Use of data beyond traditional business applications -- mining + warehousing, marketing, commerce </li></ul>
  6. 6. <ul><li>Query only, little attention to updates; extensive use of IR techniques </li></ul><ul><li>Focus shift from data to metadata; earlier, distribution applied to data only, now it also applies to metadata </li></ul><ul><li>Wrapper part of Mediator Architecture *, Metadata component of Information Brokering Architecture </li></ul><ul><li>Early work on ontology support </li></ul>Generation II * Gio Wiederhold
  7. 7. <ul><li>Increasing information overload </li></ul><ul><li>Changes in Web architecture: push,… </li></ul><ul><li>Broader variety of content with increasing amount of visual information </li></ul><ul><li>Continued standardization related to Web for representational and metadata issues (MCF, RDF, XML) and distributed computing (CORBA, Java) </li></ul><ul><li>Not just metadata, logical correlation </li></ul><ul><li>Users demand simplicity, but complexities continue to rise </li></ul>Generation III
  8. 8. <ul><li>Broader variety of users and applications; well beyond business and scientific uses (e.g., focused marketing-- more than information on the web) </li></ul><ul><li>Not just data access, but decision support through “ data mining and information discovery, information fusion, information dissemination, knowledge creation and management ”, “ information management complemented by cooperation between the information system and humans ” </li></ul>Generation III (contd)
  9. 9. Generation I and Lessons from the Federated Database Systems Research
  10. 10. Dimensions for interoperability and integration: Perspective used for Federated Databases Distribution Autonomy Heterogeneity
  11. 11. FDBS: Schema Architecture <ul><li>Model Heterogeneity: Common/Canonical Data Model Schema Translation </li></ul><ul><li>Information Sharing while preserving Autonomy </li></ul>schema translation schema integration Component DBS Local Schema Component Schema Export Schema Export Schema Export Schema Federated Schema External Schema External Schema o o o o o o o o o o o o o o o Component DBS Local Schema Component Schema
  12. 12. Heterogeneity in FDBMSs <ul><li>Hardware/System </li></ul><ul><li>instruction set </li></ul><ul><li>data representation/coding </li></ul><ul><li>configuration </li></ul><ul><li>Operating System </li></ul><ul><li>file system </li></ul><ul><li>naming, file types, operation </li></ul><ul><li>transaction support </li></ul><ul><li>IPC </li></ul><ul><li>Database System </li></ul><ul><li>Semantic Heterogeneity </li></ul><ul><li>Differences in DBMS </li></ul><ul><ul><li>data models (abstractions, constraints, query languages) </li></ul></ul><ul><ul><li>System level support (concurrency control, commit, recovery) </li></ul></ul>C o m m u n i c a t i o n 1970s 1980s
  13. 13. Characterization of Schematic Conflicts in Multidatabase Systems Schematic Conflicts Domain Definition Incompatibility Naming Conflicts Data Representation Conflicts Data Scaling Conflicts Data Precision Conflicts Default Value Conflicts Attribute Integrity Constraint Conflicts Data Value Incompatibility Known Inconsistency Temporal Inconsistency Acceptable Inconsistency Abstraction Level Incompatibility Generalization Conflicts Aggregation Conflicts Schematic Discrepancies Data Value Attribute Conflict Entity Attribute Conflict Data Value Entity Conflict Entity Definition Incompatibility Naming Conflicts Database Identifier Conflicts Schema Isomorphism Conflicts Missing Data Items Conflicts Sheth & Kashyap, Kim & Seo
  14. 14. Observations and Lessons Learnt <ul><li>“ tightly coupled” vs “loosely coupled” debate </li></ul><ul><li>“ good common data model” debate </li></ul><ul><li>“ tightly coupled” harder to build, but can give better control over data sharing, provide more transparent access, and can possibly support update; lessons learned in schema integration can be reapplied in newer situations </li></ul><ul><li>“ loosely coupled” more flexible, but generally require more user involvement </li></ul>
  15. 15. Retracing the path without learning from past expeditions <ul><li>Steps for transitioning from Data Marts to Warehouses: </li></ul><ul><li>Create consistent dimensions in the data marts </li></ul><ul><li>Create a data warehouse data model and convert data marts to it </li></ul><ul><li>Go back and build an enterprise data warehouse, then convert data marts to the new common data model and architectures </li></ul><ul><li>The above is doomed to repeat past mistakes. Integrating metadata is not easy! </li></ul>PC Week, November 24 , 1997
  16. 17. Generation 1 concern: So far (schematically), yet so near (semantically)! Generation 3 concern: So near (schematically), yet so far (semantically)!
  17. 18. Generation II and Generation III
  18. 19. Information Brokering: A Three-Level Approach Ontology Content Representation used-by abstracted-into Semantic (Domain, Application specific) Metadata (content descriptions, intentional) Data (heterogeneous types, media) used-by abstracted-into Top Down Bottom Up Emphasis from Gen.I to Gen.III
  19. 20. An Architecture for Information Brokering Information System 1 Information System N INFORMATION BROKERING Data Brokering (CORBA, HTTP, IIOP) User Query/ Information Request User Query/ Information Request User Query/ Information Request ... DATA REPOSITORIES ... DATA REPOSITORIES Inter-Vocabulary Relationships Manager Vocabulary Broker Vocabulary Broker Vocabulary Brokering Metadata Broker Metadata Repository Metadata System Metadata Broker Metadata Repository Metadata System Metadata Brokering
  20. 21. Generation 2: Limited Types of Metadata, Extractors, Mappers, Wrappers
  21. 22. Global/Enterprise Web Repositories Generation 2 Nexis UPI AP DB METADATA EXTRACTORS
  22. 23. Junglee Gen.2 Data Integration Data Publishing Publishing Rule Publisher Extraction Rules Extractor Mapping Rules Mapper Internet Wrappers (SDL Description) Text IDT Application RDBMS
  23. 24. Find Marketing Manager positions in a company that is within 15 miles of San Francisco and whose stock price has been growing at a rate of at least 25% per year over the last three years Junglee, SIGMOD Record, Dec. 1997
  24. 25. <ul><li>can automatically identify data/media type </li></ul><ul><li>can be extended at any time (pre-specified or parameterized routines) </li></ul><ul><li>can run at data source, metadata storage site or at IQ server </li></ul><ul><li>can run at pre-specified times or events, or on demand </li></ul><ul><li>can route metadata to appropriate metadatabase repositories </li></ul><ul><li>Extractors use agent & networking computing (NC) technologies and are implemented in PERL/ Java </li></ul>Extractors
  25. 26. A Classification of Metadata <ul><li>Content Independent Metadata e.g. creation-date, location, ... </li></ul><ul><li>Content Dependent Metadata e.g. size, number of colors in an image </li></ul><ul><ul><li>Content-(directly)based Metadata e.g. inverted lists, doc vectors </li></ul></ul><ul><ul><li>Content-descriptive Metadata </li></ul></ul><ul><ul><ul><li>Domain Independent (structural) Metadata </li></ul></ul></ul><ul><ul><li>e . g. parse tree of a C++ program, HTML/SGML DTDs </li></ul></ul><ul><ul><ul><li>Domain Specific Metadata scale, coordinate, land-cover, relief (GIS Domain), area, population (Census Domain), concept descriptions from Domain Specific Ontologies </li></ul></ul></ul>Move in this direction to tackle information overload !!
  26. 27. Query Processing and Information Requests <ul><li>traditional queries based on keywords </li></ul><ul><li>attribute-based queries </li></ul><ul><li>content-based queries </li></ul><ul><li>'high-level' information requests involving ontology-based, iconic, mixed-media, and media-independent information requests </li></ul><ul><li>user selected ontology, use of profile </li></ul>E.g., Kabila’s political activities (in all media) Generation 2 Generation 3
  27. 28. VisualHarness . . Image Data Color Comp Texture Structure Other Attributes VIR Extraction Null Image Metadata for combined access User Query VH Results
  28. 29. Metadata Brokering in VisualHarness
  29. 30. VisualHarness  An Example
  30. 31. What else can Information Brokering do?
  31. 32. <ul><li>WWW </li></ul><ul><li>A confusing heterogeneity of media, formats (Tower of Babel) </li></ul><ul><li>Information correlation using physical (HREF) links at the extensional data level </li></ul><ul><li>Location dependent browsing of information using physical (HREF) links => User has to keep track of information content !! </li></ul><ul><li>WWW+ Information Brokering </li></ul><ul><li>Domain Specific Ontologies as “semantic conceptual views” </li></ul><ul><li>Information correlation using concept mappings at the intensional concept level </li></ul><ul><li>Browsing of information using terminological relationships across ontologies => Higher level of abstraction, closer to user view of information !! </li></ul>
  32. 33. Ontologies for semantic interchange <ul><li>Need for “ transcending ” local subject areas/domains => Design Adaptable systems which “ adapt/adjust ” themselves in the face of vocabularies from different domains </li></ul><ul><li>Coordination and interrelation of models across domains One approach => utilize terminological relationships across concepts in ontologies </li></ul><ul><li>Specification languages for ontologies: </li></ul><ul><ul><li>Description Logics, Rule-based Languages </li></ul></ul><ul><ul><li>Support for mechanisms for Coordination and Correlation, viz., representation and reasoning with terminological relationships </li></ul></ul>
  33. 34. The InfoQuilt Project http://lsdis.cs.uga.edu/infoquilt
  34. 35. Correlating Data on the Web today <ul><li><TITLE> A Scenic Sunset at Lake Tahoe </TITLE> </li></ul><ul><li><p> </li></ul><ul><li>Lake Tahoe is a popular tourist spot and <A HREF = “http://www1.server.edu/lake_tahoe.txt”> some interesting facts </A> are available here. The scenic beauty of Lake Tahoe can be viewed in this photograph: <center> <IMG SRC=“http://www2.server.edu/lake_tahoe.img”> </center> </li></ul>Correlation achieved by using physical links Done manually by user publishing the HTML document
  35. 36. MREF Metadata Reference Link -- complementing HREF Creating “logical web” through Media Independent Metadata based Correlation
  36. 37. Metadata Reference Link (<A MREF …>) <ul><li><A HREF=“URL”> Document Description </A> </li></ul><ul><li>physical link between document (components) </li></ul><ul><li><A MREF KEYWORDS=<list-of-keywords>; THRESH=<real>> Document Description </A> </li></ul><ul><li><A MREF ATTRIBUTES(<list-of-attribute-value-pairs>)> Document Description </A> </li></ul><ul><li><A MREF(<parameterized_routine(….)> Document Description </A> </li></ul>
  37. 38. Correlation based on Content-descriptive Metadata Some interesting <A MREF KEYWORDS=“scenic waterfall mountain”; THRESH = 0.9 > information on scenic waterfalls </A> is available here. Content Descriptive Metadata Marina wonderland You are seeing the nature’s beauty of marina wonderland situated in the coastal region of the southern part of India. It consists of huge mountains and water flowing in between the mountains. WAIS LSI Glimpse SMART … . … . waterfall.gif (Data) Full Text Indexing
  38. 39. Correlation based on Content-based Metadata height, width and size Some interesting <A MREF KEYWORDS= “scenic waterfalls”; THRESH = 0.9; ATTRIBUTES (major-color = ‘blue’) > information on scenic waterfalls </A> is available here. waterflow.gif (Data) Metadata Storage waterflow.gif …… gif …… ppm Major component(RGB) Blue Content based Metadata Content Dependent Metadata
  39. 40. Metadata, Domain Specific Ontologies Get the titles , authors , documents , maps published by the United States Geological Service (USGS) about regions having a population greater than 5000, area greater than 1000 acres having a low density urban area land cover domain specific metadata: terms chosen from domain specific ontologies What is Metadata ? - data/information about data - useful/derived properties of media - properties/relationships between objects What are Ontologies ? - collection of terms, definitions and their interrelationships - specification of a representational vocabulary for a shared domain of discourse
  40. 41. TIGER/Line DB Population: Area: Boundaries : Land cover: Relief: Census DB Image/Map DB Regions (SQL) Boundaries Image Features (image processing routines) Repositories and the Media Types
  41. 42. Domain Specific Correlation <ul><li>Potential locations for a future shopping mall identified by all regions having a population greater than 500 and area greater than 50 sq ft having an urban land cover and moderate relief <A MREF ATTRIBUTES(population > 500; area > 50; region-type = ‘block’; land-cover = ‘urban’; relief = ‘moderate’)> can be viewed here </A> </li></ul><ul><li>=> media-independent relationships between domain specific metadata : population, area, land cover relief </li></ul><ul><li>=> correlation between image and structured data at a higher domain specific level as opposed to physical “link-chasing” in the WWW </li></ul>
  42. 45. InfoQuilt Architecture (partial) Media Independent Information Requests [Browsing Collections, Keyword-based queries, Attribute-based queries] Correlation Server Media and Domain specific Extractor Agents ... IQR: Metadata & Domain Knowledge Repository and Registry loc, type, author Attr. Metadata Parameterized Routines InfoQuilt Server KnowledgeBase Other InfoQuilt Servers Domain Knowledge Indices Text, Image, Audio, Video media repositories Wrapper Wrapper Wrapper
  43. 46. What next (after comprehensive use of metadata) ? <ul><li>Context, context, context </li></ul><ul><li>Semantic Proximity </li></ul><ul><ul><li>domain </li></ul></ul><ul><ul><li>context </li></ul></ul><ul><ul><li>modeling/abstraction/representation </li></ul></ul><ul><ul><li>state </li></ul></ul><ul><li>Characterizing Loss of Information incurred due to differences in vocabulary </li></ul>BIG challenge: identifying relationship or similarity between objects of different media, developed and managed by different persons and systems
  44. 47. A Semantic Taxonomy Semantic Proximity Semantic Resemblance Semantic Relevance Semantic Relationship Semantic Equivalence Semantic Incompatibility
  45. 48. Tools to support semantics ontologies profiles context domain-specific metadata
  46. 49. Computing Communication Information Knowledge Data Decision Connectivity and Data Access Interoperability Cooperation
  47. 50. Computing Communication Information Knowledge Data Decision Connectivity Interoperability Cooperation Interoperability in the ‘80s System level interoperability like TCP/IP. Standard communication channels, data exchange formats, etc. Basic infrastructural work for higher level interoperability . HTTP, IIOP, TCP/IP
  48. 51. Computing Communication Information Knowledge Data Decision Connectivity Interoperability Cooperation Interoperability in the ‘90s Information level interoperability. Standards evolve that go beyond connectivity and define information standards. Systems start exchanging metadata (MCF,RDF,..). Business Objects, CORBA, DCOM, EDI
  49. 52. Computing Communication Information Knowledge Data Connectivity Interoperability Cooperation Where we are headed Semantic interoperability where systems share ontologies and knowledge. Systems and human can cooperate in decision making and can generate new knowledge as a collective entity.
  50. 53. Cognition Heuristics Learning Semantics KNOWLEDGE Introspection Deduction
  51. 54. Cooperative Information Systems Collective exploitation of complementary technologies Information Management Coordination <ul><li>Scheduling </li></ul><ul><li>Workflow </li></ul>Collaboration <ul><li>Video Conferencing </li></ul><ul><li>Whiteboarding </li></ul><ul><li>Application sharing </li></ul>
  52. 55. Computing Communication Information Interoperablity Knowledge Data Infocosm Cooperating Information Systems
  53. 56. Summary <ul><li>We have addressed many data level (schematic, representational,…) issues so far </li></ul><ul><li>We are in a good position to solve additional issues using metadata level; need to support domain-specific metadata and “media-independent” information requests, qualified by use of ontologies </li></ul><ul><li>some challenges remain: e.g., consistency of metadata </li></ul>
  54. 57. Agenda for Research <ul><li>Interoperation not at systems level, but at informational and possibly knowledge level </li></ul><ul><ul><li>traditional database and information retrieval solutions do not suffice </li></ul></ul><ul><ul><li>need to understand context; measures of similarities </li></ul></ul><ul><li>Need to increase impetus on semantic level issues involving terminological and contextual differences, possible perceptual or cognitive differences in future </li></ul><ul><ul><li>information systems and humans need to cooperate, possible involving a coordination and collaborative processes </li></ul></ul>
  55. 58. http://lsdis.cs.uga.edu [See publications on Metadata, Semantics, InfoHarness/InfoQuilt] [email_address]

×