The document discusses search computing and the SeCo team's efforts to enable complex, multi-domain queries across disparate data sources. It presents motivating examples that require composing information from different semantic domains. The overall framework involves analyzing high-level queries, breaking them into sub-queries to specific domains, planning and executing low-level queries, then merging and transforming results. The architecture supports an incremental prototyping approach, starting with basic query execution and expanding to planning, mapping, and presentation of results. CAISE work focuses on service registration and representation of resources as entities and connections in a conceptual service mart.
A Service-Based Architecture for Multi-domain Search on the WebAlessandro Bozzon
Those slides were presented at the 8th International Conference on Service Computing (ICSOC 2010, San Francisco), and they relates to the research paper "A Service-Based Architecture for Multi-domain Search on the Web" authored by Alessandro Bozzon, Marco Brambilla, Francesco Corcoglioniti, and Salvatore Vadacca
Slides from my presentation at the Amsterdam Data Science seminar on City Analytics.
http://amsterdamdatascience.nl/event/amsterdam-data-science-seminar-city-analytics/
More information about the social glass project: www.social-glass.org
Slides of the presentation given at the 22nd International Conference on the World Wide Web.
URL: http://www2013.org/program/561-reactive-crowdsourcing/
More information on the Crowdsearcher project available at
crowdsearcher.search-computing.com
A Service-Based Architecture for Multi-domain Search on the WebAlessandro Bozzon
Those slides were presented at the 8th International Conference on Service Computing (ICSOC 2010, San Francisco), and they relates to the research paper "A Service-Based Architecture for Multi-domain Search on the Web" authored by Alessandro Bozzon, Marco Brambilla, Francesco Corcoglioniti, and Salvatore Vadacca
Slides from my presentation at the Amsterdam Data Science seminar on City Analytics.
http://amsterdamdatascience.nl/event/amsterdam-data-science-seminar-city-analytics/
More information about the social glass project: www.social-glass.org
Slides of the presentation given at the 22nd International Conference on the World Wide Web.
URL: http://www2013.org/program/561-reactive-crowdsourcing/
More information on the Crowdsearcher project available at
crowdsearcher.search-computing.com
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerDatabricks
Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads.
Virtualizing Latency Sensitive Workloads and vFabric GemFireCarter Shanklin
This presentation was made by Emad Benjamin of VMware Technical Marketing. Normally I wouldn't upload someone else's preso but I really insisted this get posted and he asked me to help him out.
This deck covers tips and best practices for virtualizing latency sensitive apps on vSphere in general, and takes a deep dive into virtualizing vFabric GemFire, which is a high-performance distributed and memory-optimized key/value store.
Best practices include how to configure the virtual machines and how to tune them appropriately to the hardware the application runs on.
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
Keynote talk at the International Conference on Supercoming 2009, at IBM Yorktown in New York. This is a major update of a talk first given in New Zealand last January. The abstract follows.
The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.
Large-scale projects development (scaling LAMP)Alexey Rybak
This 8-hours tutorial was given at various conferences including Percona conference (London), DevConf (Moscow), Highload++ (Moscow).
ABSTRACT
During this tutorial we will cover various topics related to high scalability for the LAMP stack. This workshop is divided into three sections.
The first section covers basic principles of shared nothing architectures and horizontal scaling for the app//cache/database tiers.
Section two of this tutorial is devoted to MySQL sharding techniques, queues and a few performance-related tips and tricks.
In section three we will cover the practical approach for measuring site performance and quality, porviding a "lean" support philosophy, connecting buesiness and technology metrics.
In addition we will cover a very useful Pinba real-time statistical server, it's features and various use cases. All of the sections will be based on real-world examples built in Badoo, one of the biggest dating sites on the Internet.
Neural Search Comes to Apache Solr_ Approximate Nearest Neighbor, BERT and Mo...Sease
The first integrations of machine learning techniques with search allowed to improve the ranking of your search results (Learning To Rank) – but one limitation has always been that documents had to contain the keywords that the user typed in the search box in order to be retrieved. For example, the query “tiger” won’t retrieve documents containing only the terms “panthera tigris”. This is called the vocabulary mismatch problem and over the years it has been mitigated through query and document expansion approaches.
Neural search is an Artificial Intelligence technique that allows a search engine to reach those documents that are semantically similar to the user’s query without necessarily containing those terms; it avoids the need for long lists of synonyms by automatically learning the similarity of terms and sentences in your collection through the utilisation of deep neural networks and numerical vector representation.
Presentation as held at the "Workshop on Knowledge Evolution and Ontology Dynamics" co-located with ISWC 2011. Related to the paper http://ceur-ws.org/Vol-784/evodyn1.pdf
In this presentation we review some of the research problems we address at EPFL in the area of sensor data management. At the level of infrastructure we have developed a middleware to seamlessly integrate, aggregate and analyze heterogeneous sensor data streams in real-time, a WIKI based repository supporting the cooperative management of the metadata associated with sensor deployments and cloud-based storage infrastructure. An important problem in managing sensor data is their efficient storage and transmission using compression techniques. To that end we apply model-based compression methods. For analyzing sensor data, we have developed methods to dynamically estimate the variability, which can be readily used for outlier detection, and to extract semantic features from GPS sensor data streams. We also investigate techniques for trading off between the accuracy of the sensor data obtained and the degree of privacy preservation that can be maintained.
The Sensor Data Management presentation was presented by Karl Aberer (Ecole Polytechnique Federale de Lausanne) at the PlanetData project Meeting on February 28 - March 4, 2011 in Innsbruck, Austria.
Dense Retrieval with Apache Solr Neural Search.pdfSease
Neural Search is an industry derivation from the academic field of Neural information Retrieval. More and more frequently, we hear about how Artificial Intelligence (AI) permeates every aspect of our lives and this includes also software engineering and Information Retrieval.
In particular, the advent of Deep Learning introduced the use of deep neural networks to solve complex problems that could not be solved simply by an algorithm. Deep Learning can be used to produce a vector representation of both the query and the documents in a corpus of information. Search, in general, comprises of performing four primary steps:
- generate a representation of the query that describes the information need - generate a representation of the document that captures the information contained in it
- match the query and the document representations from the corpus of information
- assign a score to each matched document in order to establish a meaningful document ranking by relevance in the results.
With the Neural Search module, Apache Solr is introducing support for neural network based techniques that can improve these four aspects of search.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerDatabricks
Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads.
Virtualizing Latency Sensitive Workloads and vFabric GemFireCarter Shanklin
This presentation was made by Emad Benjamin of VMware Technical Marketing. Normally I wouldn't upload someone else's preso but I really insisted this get posted and he asked me to help him out.
This deck covers tips and best practices for virtualizing latency sensitive apps on vSphere in general, and takes a deep dive into virtualizing vFabric GemFire, which is a high-performance distributed and memory-optimized key/value store.
Best practices include how to configure the virtual machines and how to tune them appropriately to the hardware the application runs on.
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
Keynote talk at the International Conference on Supercoming 2009, at IBM Yorktown in New York. This is a major update of a talk first given in New Zealand last January. The abstract follows.
The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.
Large-scale projects development (scaling LAMP)Alexey Rybak
This 8-hours tutorial was given at various conferences including Percona conference (London), DevConf (Moscow), Highload++ (Moscow).
ABSTRACT
During this tutorial we will cover various topics related to high scalability for the LAMP stack. This workshop is divided into three sections.
The first section covers basic principles of shared nothing architectures and horizontal scaling for the app//cache/database tiers.
Section two of this tutorial is devoted to MySQL sharding techniques, queues and a few performance-related tips and tricks.
In section three we will cover the practical approach for measuring site performance and quality, porviding a "lean" support philosophy, connecting buesiness and technology metrics.
In addition we will cover a very useful Pinba real-time statistical server, it's features and various use cases. All of the sections will be based on real-world examples built in Badoo, one of the biggest dating sites on the Internet.
Neural Search Comes to Apache Solr_ Approximate Nearest Neighbor, BERT and Mo...Sease
The first integrations of machine learning techniques with search allowed to improve the ranking of your search results (Learning To Rank) – but one limitation has always been that documents had to contain the keywords that the user typed in the search box in order to be retrieved. For example, the query “tiger” won’t retrieve documents containing only the terms “panthera tigris”. This is called the vocabulary mismatch problem and over the years it has been mitigated through query and document expansion approaches.
Neural search is an Artificial Intelligence technique that allows a search engine to reach those documents that are semantically similar to the user’s query without necessarily containing those terms; it avoids the need for long lists of synonyms by automatically learning the similarity of terms and sentences in your collection through the utilisation of deep neural networks and numerical vector representation.
Presentation as held at the "Workshop on Knowledge Evolution and Ontology Dynamics" co-located with ISWC 2011. Related to the paper http://ceur-ws.org/Vol-784/evodyn1.pdf
In this presentation we review some of the research problems we address at EPFL in the area of sensor data management. At the level of infrastructure we have developed a middleware to seamlessly integrate, aggregate and analyze heterogeneous sensor data streams in real-time, a WIKI based repository supporting the cooperative management of the metadata associated with sensor deployments and cloud-based storage infrastructure. An important problem in managing sensor data is their efficient storage and transmission using compression techniques. To that end we apply model-based compression methods. For analyzing sensor data, we have developed methods to dynamically estimate the variability, which can be readily used for outlier detection, and to extract semantic features from GPS sensor data streams. We also investigate techniques for trading off between the accuracy of the sensor data obtained and the degree of privacy preservation that can be maintained.
The Sensor Data Management presentation was presented by Karl Aberer (Ecole Polytechnique Federale de Lausanne) at the PlanetData project Meeting on February 28 - March 4, 2011 in Innsbruck, Austria.
Dense Retrieval with Apache Solr Neural Search.pdfSease
Neural Search is an industry derivation from the academic field of Neural information Retrieval. More and more frequently, we hear about how Artificial Intelligence (AI) permeates every aspect of our lives and this includes also software engineering and Information Retrieval.
In particular, the advent of Deep Learning introduced the use of deep neural networks to solve complex problems that could not be solved simply by an algorithm. Deep Learning can be used to produce a vector representation of both the query and the documents in a corpus of information. Search, in general, comprises of performing four primary steps:
- generate a representation of the query that describes the information need - generate a representation of the document that captures the information contained in it
- match the query and the document representations from the corpus of information
- assign a score to each matched document in order to establish a meaningful document ranking by relevance in the results.
With the Neural Search module, Apache Solr is introducing support for neural network based techniques that can improve these four aspects of search.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Search Computing
1. Search Computing
The SeCo Team
Stefano Ceri, Adnan Abid, Mamoun Abu Helu, Davide Barbieri,
Daniele Braga, Marco Brambilla, Alessandro Bozzon, Alessandro
Campi, Sofia Ceppi, Francesco Corcoglioniti, Emanuele Della Valle,
Davide Eynard, Piero Fraternali, Nicola Gatti, Giorgio Ghisalberghi,
Michael Grossniklaus, Davide Martinenghi, Marco Masseroli,
Maristella Matera, Chiara Pasini, Elena Pellizzotti, Stefania Ronchi,
Marco Tagliasacchi, Luca Tettamanti, Salvatore Vadacca, Riccardo
Volonterio, Serge Zagorac
2. Genesis of Search Computing
My “Gong Show” challenge at 2003 Lowell Workshop:
“Find an ethnical restaurant in a nice place close to Milano” .
Logically a composition of domains:
– Restaurants (ethnical)
– Geo-locations (nice place close to Milano)
Composing maps with “geo-located” information is now
solved by all search engines …
… but in general no system is capable of composing
arbitrary semantic domains
Database Management Prof. Stefano Ceri
3. Motivating Examples 3
“Who are the strongest candidates in Europe for
competing on software ideas?”
“Who is the best doctor who can cure insomnia in a
close-by hospital?”
“Where can I attend an interesting scientific conference in
my field and at the same time relax on a beautiful beach
nearby?”
Database Management Prof. Stefano Ceri
4. Their Common Aspect 4
Multi-domain queries
Individual answers are on the Web
A knowledgeable user would do the query step-by-step:
– Search database conferences, get their city
– Check that the city average temperature is warm enough
– Search low-cost flights via a broker for that city
– Search luxury hotels via another broker
We want a system for supporting this search process
– Build several “solutions” which already integrate all dimensions
– Rank “solutions” according to a global rank function and output
results in rank order
– Support user-friendly query definition and result browsing
– Add search domains while the search proceeds
– Possibly change the relative weight of each ranking
Database Management Prof. Stefano Ceri
6. Search Computing architecture: overall view 6
Front End
High level query
“Where can I attend a DB
scientific conference close to
High-Level Query
a beautiful beach reachable
Presented results
Final User
Query Analysis with cheap flights?” ESWC-Crete-Olympic
Results
Sub query 1 CAISE- Hammamet – Alitalia
Sub query Cache
2
“Where can I attend a DB Sub query 3 TOOLS-Malaga-EasyJet
“place close to
scientific conference?” “place reachable with
Sub-queries
a beautiful beach?”
Query To Domain
cheap flight?” Result Cache
Low level query 1 Mapper
Cache
Transformation
ConfSearch(“DB”,placeX,dateY)
Low level query 2
TourSearch(“Beach”,PlaceX) queries 3 Merged Results
Low level query
Low-level
Flight(“cost<200”,PlaceX,DateY) Results
Query Planner
Cache
Query plan
Concrete
Query Plan
Query Engine
WS-Framework Main Query flow
Cache
OP 1 OP 2 ... OP N Services invocations Cache
and operators execution
<Uses> relation
Domain Domain Service WS
Framework Repository Repository World
Cache
Database Management Prof. Stefano Ceri
7. Search Computing architecture: incremental prototyping
7
Prototype 4:
Front End
High level queries
Concrete Query Plan
Low-level queries
Prototype 3:
Sub-queries
High-Level Query
Mapping and Final User
presentation Query Analysis Results
Cache
• mapping to domains
• presentation of results Sub-queries
Cache
Admin Interface
Query To Domain Result
Prototype 2: Mapper Transformation
Cache
Planning
Low-level queries
Merged Results
• Automatic optimized
query planning Query Planner
Cache
Concrete
Prototype 1: Query Plan
Core behaviour of the
Query Engine
system. WS-Framework
OP 1 OP 2 ... OP N Cache
Cache
• Engine-based execution
of queries
• Domain repository <Uses> relation
• Service repository
• Coarse result
presentation
Domain Domain Service WS
Framework Repository Repository World
Cache
Database Management Prof. Stefano Ceri
8. CAISE FOCUS on: Service Registration 8
Service Marts:
• Conceptual
representation of
resources as entities
and connections
• Logical representation of
signatures
• Physical representation
as service
implementations
Database Management Prof. Stefano Ceri
9. CAISE FOCUS on: Front-end 9
Liquid Query Front End
Client-side framework
High-Level Query
for configuration and Final User
automatic rendering Query Analysis Results
of query and result Cache
interfaces Sub-queries
Cache
User interaction Query To Domain
Mapper
Result
Transformation
Cache
primitives that allow to
Low-level queries
perform explanatory Merged Results
search Query Planner
Cache
Concrete
Query Plan
Query Engine
WS-Framework
OP 1 OP 2 ... OP N Cache
Cache
Domain Domain Service WS
Framework Repository Repository World
Cache
Database Management Prof. Stefano Ceri
10. CAISE FOCUS on: Development Process 10
Development Support
Deploy
<<implements>> <<deploys>>
Time
Search Services SeCo platform
Environment Service
Developer
SeCo
Expert
Tools supporting
Service Publishing Time
Wrapping
Service Registration <<implements>>
Query Design <<defines>> Materialization /
Normalization
Performance Monitoring Service
Publisher
<<uses>>
<<performs>> Registration of
Service Mart <<produces>>
Service Mart
<<uses>> Repository
Config.
Time
<<defines>> Liquid Query
<<produces>>
Template
Expert
User
Execution Time
<<uses>>
<<submits>> Liquid Query User Interface
Specification
<<manipulates>>
<<uses>>
Final User Liquid Result
Database Management Prof. Stefano Ceri
12. Service Registration in SeCo
Objective: providing a framework for registering services as
first-class citizens within SeCo
=> Service Marts
High-level abstractions of “real world entities” that provide a simple
interface to users and hide implementation details
Inspired by Data Marts, a data modeling pattern used in data
warehousing
Each Service Mart can have multiple modalities of data access and
can be mapped to multiple service implementations, possibly
offered by different providers
=> Connection Patterns
High-level abstractions of “real world relationships” that provide a
simple interface to users and hide implementation details
Built by means of attributes that share the same domains
Database Management Prof. Stefano Ceri
13. Service Marts – Conceptual Level
Every SM definition includes a name and a collection of the exposed
attributes, i.e. the attributes of the real world object described by the SM
Movie(Title, Director, Year, Language, Genres(Genre), Actors(Name, Sex))
Atomic, single valued, typed attributes
Repeating groups (multi-valued, typed attributes)
Each “repeating group” is a non-empty set of typed sub-attributes
that collectively defines a property of the service mart
The model choices are:
To support structural complexity with only one level of nesting
(rather than an arbitrary level of nestings)
To avoid explicit descriptions of relationship (using repeating
groups for M:N relationships)
Database Management Prof. Stefano Ceri
14. Service Marts – Logical Level
At this level, each SM is associated with one or more Access Patterns, i.e.:
Movie1(TitleO, DirectorO, ScoreRO, YearO, LanguageI, Genres.GenreO, Actors
.NameO , Actors.SexO, Genres.GenreI)
Movie2(TitleI, DirectorO, YearO, LanguageO, Genres.GenreO, Actors.NameO
, Actors.SexO)
Access patterns contain adorned attributes, i.e. attributes tagged with one of
the following:
I, if they are input attributes
O, if they are Output attributes
R, if they are attributes used for ranking – they may or may not be visible in output
Movie1 makes access to movies by Language and Genre (i.e., “action movies
in English”) and results are ranked by Score (a new attribute).
Movie2 makes access to movies by Title (e.g. “Ben Hur”). We expect few
(zero, one, more) results which are not ranked.
Database Management Prof. Stefano Ceri
15. Service Marts – Physical Level
At this level, every Access Pattern can match different
Service Implementations, having:
Physical URI to be called
Physical properties which are specific to the implementation
Mapping between logical and physical parameters
IMDBMovie1(MovieTitleO, DirectorO, StarsRO, YearO, LanguageI, Genres.G
enreI, Actors.NameO , Actors.GenderO)
IMDBMovie AP: Movie1 URI: http://...
TTL=6000, chunksize=10, cacheable=true, exposed=false, ...
Title Director Score Year Language ...
MovieTitle Director Stars Year Lang ...
Database Management Prof. Stefano Ceri
16. External and Selector Attributes
external attributes, for supporting access and ranking
SM Movie(Title, Director, Year, Language, …)
AP Movie1: TitleO | DirectorO | YearO | … | ScoreRO | GenreI
AP Movien: TitleO | DirectorO | YearO | … | TitleI
External attributes
selector attributes, for supporting choices among service implementations
SM Movie(Title, Director, Year, Language, …)
Language
SI Movie Implementation 1
... Selector
SI Movie Implementation n
Database Management Prof. Stefano Ceri
17. Connection Patterns
Connections between marts only exist in terms of attributes that share the same
domains, on different levels of abstraction:
Conceptually by a nondirected edge with a name:
PlayingMovie(Movie,Theatre)
Movie Theatre
Logically by an edge (possibly directed) with name and join condition:
PlayingMovie(Movie,Theatre): (Title=Movie.Title)
Movie4 Theatre2
Database Management Prof. Stefano Ceri
18. Connection Patterns – Logical Level
Directed edge: Information is “piped” from one access pattern to
another, along connection attributes which are in output in the first service
and in input in the second service -> PIPE JOIN
Movie1 Title Director Score Year Language A.Name A.Sex G.Genre
Theatre1 Name Address M.Start M.Title
Database Management Prof. Stefano Ceri
19. Connection Patterns – Logical Level
Undirected edge: results are produced by both access patterns in output and
then joined -> PARALLEL JOIN
Movie1 Title Director Score … … G.Genre
Theatre1 Name Address M.Start M.Title
Database Management Prof. Stefano Ceri
20. Join of two Services, Pipe Version, NY City
Search only in NY
Movie Theatre
Service Mart Service Mart
Movie1 Movie2 Theatre1
Access Access Access
pattern pattern pattern
IMDB2
Service
Interface
IMDB1 Hyperrev1
Service Service
Interface Interface
Google1 NYLocalSearch
Service Service
Interface Interface
Database Management Prof. Stefano Ceri
21. 21
JOIN OF TWO SEARCH
SERVICES
Database Management Prof. Stefano Ceri
22. JOIN of Web Services
Input: items resulting from TWO web service
calls, possibly ranked
Output: composed items resulting from the
concatenation of matching items, presented in a
“global ranking order”
Matching condition using:
– value equality,
– partial set matching
– term matching within a vocabulary
…..
Services are known, their matching function is
predefined: this is not service discovery!
Database Management Prof. Stefano Ceri
23. Join 23
Service X Service Y
bx5 by5
bx4 by4
bx3 by3
bx2 by2
bx1 by1
r1
r2
r3
Database Management Prof. Stefano Ceri
24. Matching items 24
Database Management Prof. Stefano Ceri
25. Choice of the join strategies
The join search space
– Different explorations for different joins methods under different
assumptions and with different guarantees
Chunksize
Chunk
tij
Any exploration trajectory Candidate join result
for this space is a join strategy
Database Management Prof. Stefano Ceri
30. Supporting value similarity
Concept of “nearness” is widely implemented depending
on different contexts, such as:
Lexical near (similar strings)
Spatial near (between addresses/geo locations)
Temporal near (between dates/times)
Economic near (between costs)
Context is defined according to the attributes involved
=> Semantics of nearness built bottom-up, starting from the
physical layer (available services) up to the conceptual
one.
Database Management Prof. Stefano Ceri
31. Similarity comes from Shared Domains
The attribute
“address” is shared
by the 4 entities. Its restaurant apartment
semantic Address Address
type, describing a
location, enables Spatial
“nearness” Near
connections between
each pair of entities
Address Address
(i.e. addresses can
be compared for hotel theatre
“nearness” within the
same
city, country, …)
Database Management Prof. Stefano Ceri
32. Supporting Nearness within Services
Several physical services natively support ranking by
distances (e.g. GoogleMovies)
E.g.: GoogleMovies receives the user address as
input, and returns theatres ranked by distance, each one
with its address as output. UserAddress and Distance
are external attributes.
GoogleMovies(UserAddressI, DistanceR |
NameO, AddressO, Movie.TitleI, Movie.StartTimeO)
GoogleMovies AP: Theatre1 URI: http://...
TTL=6000, chunksize=10, cacheable=true, provides=Spatial Near
UserAddress Name Address M.Title M.StartTime ...
IAddr Name OAddr MovieTit MovieTime ...
Database Management Prof. Stefano Ceri
33. “Nearness” Support within Services
Theatre Restaurant
Spatial Near
Restaurant2 Address Name Cuisine Price
Spatial near
Theatre1 UserAddress Name Address M.Title M.StartTime Distance
GoogleMovies AP: Theatre1 URI: http://...
TTL=600, chunksize=10, cache=1, provides=Spatial Near
UserAddr Name Address M.Title ...
Addr Name Addr MovieTit ...
Database Management Prof. Stefano Ceri
34. Nearness Services within the Execution Engine
Ad-hoc services providing the notion of distance at the physical level require
two domain values as input and produce their distance as output
Two input attributes to specify two values of the domain
One output attribute specifies the distance in given units
SpatialNear System URI: http://...
TTL=600, chunksize=1, cacheable=1, ...
Input1, Input2: Coordinates Output: Distance (Km)
Database Management Prof. Stefano Ceri
35. Supporting Nearness within the Execution Engine
Theatre Restaurant
Spatial Near
Restaurant2 Address Name Cuisine Price
Theatre1 Address Name M.Title M.StartTime
Spatial Near Addr1 Addr2 Distance
SpatialNear System URI: http://...
TTL=600, chunksize=1, cacheable=1, ...
Input1, Input2: Coordinates Output: Distance (Km)
Database Management Prof. Stefano Ceri
36. Join of three Services at the three Levels in NY
Search only in NY
Movie Theatre Restaurant
Service Mart Service Mart Service Mart
Spatial Near
Movie1 Movie2 Theatre1 Rest1 Rest2
AP
Access Access Access Access
providing
pattern pattern pattern pattern
spatial near
IMDB2 Yahoo1 Yahoo2
Service Service Service
Interface Interface Interface
IMDB1
Service
Interface Hyperrev1 Google1 NYLocalSearch
Service Service Service
Interface Interface Interface
Database Management Prof. Stefano Ceri
37. Three Levels with Connection Semantics
Services Connections
Name (with associated
Conceptual Service Mart
semantics)
Bindings between SM and
AP attributes, plus
definition of extra
attributes
Join attributes,directed vs
Logical Access Pattern undirected edge (with nearness
service APs added as needed)
Bindings between AP
attributes and SI
parameters
Service Interface (with
Physical associated semantics and Nearness Services
with system services)
Database Management Prof. Stefano Ceri
38. Resource graph
Specialized way for describing search service based
knowledge available on the web [ER
model, ontology, class diagram?]
News Restaurant
Exhibition
...
Piece
...
Concert
...
Artist
... Photo ...
Hotel
Movie
... Metro Station
Theatre
Landmark
... ShoppingCenter
...
Database Management Prof. Stefano Ceri
40. SeCo development process
Search Service
and Registration Development
Main Roles:
• Service
developer Service developer Implement search service
• Service
publisher
Adaptation
Service
• Expert user Service publisher Wrap or materialize Register service
• SeCo expert service mart and interface
Dichotomy:
Configuration
Application
• Top-down Service Mart model
vs. Expert user Design Liquid Query Template
Bottom-up
• Run time Manual optimization
needed?
N
Liquid Query model
Y
vs.
Refinement
Query Plan
Design time Query Plan model
SeCo expert Panta Rhei plan refinement
Database Management Prof. Stefano Ceri
41. The service registration process
Service
Description
SM Identification
Buttom up Strategy
YES NO
Some SM
retrieved
?
YES
SM CREATION
Modification Hybrid Strategy
of the SM
structure? SM UPDATE
NO
Associated SI Update
Top down Strategy (new connections)
SM MAPPING
AP CREATION
Service Physical
Description
END
Database Management Prof. Stefano Ceri
42. The SM Creation process, with semantic hints
SM CREATION
Movie(Title, Director, Score, Year, Genres(Genre),
Openings(Country, Date), Actors(Name))
Type SM Name and attributes
conventions schema definition
Movie: S: (n) movie, film, picture, moving
picture, moving-picture show, motion
picture, motion-picture show, picture
show, pic, flick
WN (a form of entertainment that enacts a story by
SM and attributes sound and a sequence of images giving the
Semantical Description illusion of continuous movement) "they went to
Synsets (and tags?) a movie every Saturday night";
Automatic recommendation
of connectable SMs
Director: S: (n) film director, director
(the person who directs the making of a film)
SM1
Connection patterns Shows(Movie, Theatre): [(Title=Title)]
Theatres (CP) definition
SMn Defined CP: Shows Textual_near
Possible CP: Title (String) Textual_near
Spatial_near Composition Language
Textual near operators association Year (Date) Temporal_near …
Temporal_near
Database Management Prof. Stefano Ceri
43. The SM Mapping procedure
SM MAPPING
Original
SM
Movie(Title, Director, Score, Year, …)
Director: String
Director: S: the
person who
directs the making
of a film)
f
Director (String)
SI
Selector ImdbMovie: Title | Director | Score | Year | …
Auxiliary
Selector Corresponding SM attributes
attributes attributes (i.e. query
attributes)
Database Management Prof. Stefano Ceri
44. SeCo Tools
• Online tool suite that covers the whole development
process
• Mashup-based
• Built by using state of the art technologies:
1. MVC on the client: Javascript MVC
2. UI organization and panels: Yahoo! User Interfaces
3. Diagram drawing and editing: WireIt
Database Management Prof. Stefano Ceri
50. Liquid Query
“ A new paradigm allowing users to formulate and get responses
to multi-domain queries through an exploratory information
seeking approach, based upon structured information
sources exposed as software services…”
• Composite answers obtained by aggregating search results
from various domains
• Highlight the contribution of each search service
• Join of results based on the structural information afforded by
the search service interfaces
• Refine the user query
• Re-shape the result list
http://www.search-computing.net/LQDemo/
Database Management Prof. Stefano Ceri
51. Liquid query definition
It consists of subsetting and parametrizing the resource
graph...
News Restaurant
Exhibition
...
Piece
...
Concert
...
Artist
... Photo ...
Hotel
Movie
... Metro Station
Theatre
Landmark
... ShoppingCenter
...
= inputs, outputs + GR = global ranking
Database Management Prof. Stefano Ceri
52. Liquid query definition
... And then characterizing the user interaction
News Restaurant
Exhibition
Concert
Artist
Photo
Hotel
Expand
Plus: Metro Station
• Parametrization of global ranking
• Data visualization options
• .. and so on
Database Management Prof. Stefano Ceri
57. Overview
The tools is aimed at developers and permits to compose, plan and
run a SeCo query
Four panels, one for each query processing phase:
Query Logical Physical Query
composition planning planning execution
Splashscreen!
Database Management Prof. Stefano Ceri
58. Query composition (1)
Service interface browser
• lists registered service interfaces
• Input and output parameters are listed
Selected service’s statistics
• collected service statistics are displayed
• statistics may be edited for testing
purposes
Database Management Prof. Stefano Ceri
59. Query composition (2)
User-entered datalog-like query Query optimisation parameters
• joins implicitly encoded by datalog vars • control the behaviour of the
• $vars encode query inputs provided at planner
runtime • trigger the planning process
Database Management Prof. Stefano Ceri
62. Query execution (1)
Execution session management
• a session corresponds to a single query execution,
where multiple user commands may be issued
• query input parameters are specified at session
initialisation
Execution status
• displays the current session status
• displays the status of the execution commands issued
so far
Execution commands forms
• a more-all command requires more query results
• a more-one command requires more results by
extracting more data from a specific service invoked by
the query
Database Management Prof. Stefano Ceri
63. Query execution (2)
Query results
• Displays ranked
results, as soon
as computed
Execution
timeline
• displays
activation of
execution units
(e.g. service
calls)
• useful to fine
tune the engine
and the join
strategies
Database Management Prof. Stefano Ceri
64. Query execution (3)
Service calls log
• displays service calls at the chunk granularity
• shows response times, statistics, cache behaviour
Database Management Prof. Stefano Ceri
66. Results after 18 months 68
Concepts
– Service marts, rank join methods, panta rhei, liquid query
Research results
– Springer LNCS: Search Computing Challenges and Directions
– Many publications (withVLDB,WWW), many ongoing submissions
– Filing of US Patent (top-k method, random & sequential services)
Prototypes
– Execution environment, focus on liquid query and on integration
– Design support environment, focus on mashups
Dissemination
– Fifteen keynote talks, twelve articles in the Italian press
– SeCo Web site, SeCo blog, facebook, linked-in, twitter communities
– Search Computing Graduate Course at PoliMi
Temporary research positions (1 phd, 5 post-ms, 3 post-doc)
Database Management Prof. Stefano Ceri
67. Publications
69
SeCo
- D. Braga, A. Campi, S. Ceri, A. Raffio Joining the results of heterogeneous search engines Information Systems, Vol. 33, Issues 7-8, (November-December 2008), Pages 658-680
- D. Braga, S. Ceri, F. Daniel, D. Martinenghi Optimization of Multi-Domain Queries on the Web VLDB 2008: 562-573, Auckland, New Zealand, August 2008
- D. Braga, S. Ceri, F. Daniel, D. Martinenghi Mashing Up Search Services, IEEE Internet Computing 12(5): 16-23 (2008)
- D. Braga, D. Calvanese, A. Campi, S. Ceri, F. Daniel, D. Martinenghi, P. Merialdo, R. Torlone, NGS: a framework for multi-domain query answering, ICDE Workshops 2008: 254-261
- S. Ceri, Search Computin Invited Paper, 25th International Conference on Data Engineering, Shanghai, March 29 - April 2, 2009
- D. Barbieri, A. Bozzon, D. Braga, M. Brambilla,A. Campi, S. Ceri, E. Della Valle, P. Fraternali, M. Grossniklaus, D. Martinenghi, S. Ronchi, M. Tagliasacchi Data-driven optimization of -
search service composition for answering multi-domain queries (USETIM 2009) workshop at VLDB 2009, Lyon, France, August 24-28, 2009
- M.Brambilla, S. Ceri, Engineering Search Computing Applications: Vision and Challenges The 7th joint meeting of the European Software Engineering Conference (ESEC) and the ACM
SIGSOFT Symposium on the Foundations of Software Engineering (FSE), Amsterdam, The Netherlands, August 24-28 2009
- S. Ceri Search Computing The 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Milan, Italy, September 15-18 2009
- S. Ceppi and N. Gatti, An Automated Mechanism Design Approach for Sponsored Search Auctions with Federated Search Engines In Proceedings of the 12^th Workshop on Agent-
Mediated Electronic Commerce (AMEC) in the 9^th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Toronto, Canada May 10 2010
- D. Martinenghi, M. Tagliasacchi, and S. Ceri Top-k pipe-join International Workshop on Ranking in Databases, Long Beach, USA, March 2010
- A. Bozzon, M. Brambilla, S. Ceri, P. Fraternali Liquid Query: Multi-Domain Exploratory Search on the Web WWW 2010 - 19th International World Wide Web Conference - Raleigh,
North Carolina, April 26-30 2010
- A. Campi, S. Ceri, A. Maesani, S. Ronchi Designing Service Marts for Engineering Search Computing Applications The Tenth International Conference on Web Engineering, ICWE
2010, Vienna, Austria, July 5-9 2010
Related
- M. Brambilla, S. Ceri, I. Celino, D. Cerizza, E. Della Valle, F. M. Facca, A. Turati, C. Tziviskou Experiences in the Design of Semantic Services Using Web Engineering Methods and Tools
Journal on Data Semantics 2008- A. Raffio, D. Braga, S. Ceri, P. Papotti, M. Hernandez Clip: a Visual Language for Explicit Schema Mappings International Conference on Data Engineering (ICDE), April 2008
- D. Braga, D. Calvanese, A. Campi, S. Ceri, F. Daniel, D. Martinenghi, P. Merialdo, R. Torlone A New Generation Search Engine Supporting Cross Domain Queries Italian Symposium on
Advanced Database Systems (SEBD), June 2008
- D. Braga, D. Calvanese, A. Campi, S. Ceri, F. Daniel, D. Martinenghi, P. Merialdo, R. Torlone NGS: a Framework for Multi-Domain Query Answering IIMAS, International Conference on Data
Engineering Workshops (ICDE), April 2008
- A. Raffio, D. Braga, S. Ceri, P. Papotti, M. Hernandez Clip: a Tool for Mapping Hierarchical Schemas ACM SIGMOD/PODS Conference, Demo Session, June 2008
- A. Bozzon, M. Brambilla, P. Fraternali Conceptual Modeling of Multimedia Search Applications Using Rich Process Models ICWE 2009, Springer LNCS, vol. 5648, ISBN 978-3-642-02817-5.
- E. Della Valle, S. Ceri, D. F. Barbieri, D. Braga, A. Campi A First Step Towards Stream Reasoning Future Internet Symposium (FIS) 2008, pp. 72-81.
- A. Bozzon, M. Brambilla, F. M. Facca, G. Toffetti Carughi A Conceptual Modeling Approach to Business Service Mashup Development IEEE International Conference on Web Services, ICWS
2009, Los Angeles. IEEE Press, July 2009, pp. 751 - 758.
- P. Fraternali, M. Brambilla, A. Bozzon, Model-Driven Design of Audiovisual Indexing Processes for Search-Based Applications Content-Based Multimedia Indexing, 2009, CBMI '09, IEEE Press,
ISBN: 978-1-4244-4265-2, pp. 120-125.
- D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle and M. Grossniklaus, C-SPARQL: SPARQL for Continuous Querying Proceedings of WWW 2009, 18th International World Wide Web Conference
(Poster), Madrid, Spain, April 2009
- D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle and M. Grossniklaus Continuous Queries and Real-time Analysis of Social Semantic Data with C-SPARQL
In Proceedings of SDoW 2009, 2nd ISWC Workshop on Social Data on the Web, Washington, DC, USA, October 2009
- D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle and M. Grossniklaus C-SPARQL: A Continuous Query Language for RDF Data Streams International Journal of Semantic Computing (IJSC), 2010,
World Scientific Publishing
- D. F. Barbieri, D. Braga, S. Ceri and M. Grossniklaus An Execution Environment for C-SPARQL Queries In Proceedings of EDBT 2010, 13th International Conference on Extending Database Technology,
Lausanne, Switzerland, March 2010
Database Management Prof. Stefano Ceri
68. Web Site & Blog 70
Web Site
Tech Watch Blog
Blog stats: ~ 900 absolute unique visitors in the last two months
Database Management Prof. Stefano Ceri
69. Accesses to Web Site & Blog 71
Visits: 20% USA, 18% Italy, 6% UK, 4% India, 4% Canada
Provenance
Sources
Database Management Prof. Stefano Ceri
70. Search Computing First Workshop
June 17-19, 2009 72
Database Management Prof. Stefano Ceri
71. Search Computing Challenges and Directions
(LNCS, vol. 5950, Ceri-Brambilla eds.) 73
Part 1: Vision
– Ceri: Search computing
– Baeza-Yates: Next generation search
– Weikum: Search for knowledge
Part 2: Technology Watch
– Della Valle-Buganza-Gatti: The search engine industry
– Casati-Daniel-Soi: Mashup technologies
– Baumgartner-Campi-Gottlob-Herzog: Web data extraction
– Hedeler-Belhajjame-Campi-Embury-Fernandez-Paton:Dataspaces
– Bozzon-Fraternali: Multimedia and multimodal information retrieval
Part 3: Issues in Search Computing
– Campi-Ceri-Gottlob-Ronchi: Service marts
– Braga-Campi-Grossniklaus: Join methods and query optimization
– Ilyas-Martinenghi-Tagliasacchi: Rank aggregation
– Braga-Grossinklaus-Ceri: Panta Rhei, a query execution environment
– Brambilla-Ceri-Fraternali-Manolescu: Liquid queries and liquid results
– Brambilla-Ceri: Software engineering of search computing applications
– Masseroli-Paton-Spasic: Search computing and the life sciences
Database Management Prof. Stefano Ceri
72. Second Workshop: Design Principles 74
Consolidate several ongoing research chapters touching the various
aspects of the project
Develop connections to other research projects so as to share
knowledge - and possibly build cooperations based on mutual
complementarity.
Setting internal deadlines to project evolution
– Being ready for the workshop
– Dump organisational responsibility to session chairs
Try a more discussion-oriented format
– Our view
– Guest’s views
– Panel/discussion (sometimes driven, sometimes not)
Produce Proceedings as Springer LNCS, each session contributing
to a short part
Database Management Prof. Stefano Ceri
74. Second Workshop: Sessions 76
Pre-Workshop (Milano, May 25)
– Search as a Process
– Business Models
Workshop (Como, May 26-28)
– Semantic Resource Framework
– Wrapping Technology and Ontological Annotation
– Design Tools and Mashup Languages
– Search Computing and Research Evaluation
– Query Processing
– Rank Join
– Search Computing for BioMedical Applications
– User-Centered Approach to Search Computing Applications
Post-Workshop (Milano, May 31)
– Visual Interfaces for Complex Search
Database Management Prof. Stefano Ceri
75. Looking forward 77
Establish stronger co-operation with other projects
– Both for technology and applications
Strengthen SeCo “core research”
– Cover the process lifecycle with methods & tools
– Improve result visualization and user interaction
– Use semantics in service registration and query processing
– Turn Panta Rhei into a full Service Base Management System
(SBMS) with new rank join methods, proximity, uncertainty…
Strengthen the prototypes
– Fully develop the registration environment
– Extend the execution environment, make it scalable over clouds
– Extend the liquid interface, cover mobile interfaces
Put a “killer” application online (usable!)
Explore exploitation options
Database Management Prof. Stefano Ceri
76. SeCo and Caise 78
Starting point: over 20% of user interactions on the Web
are from search engines, over 95% of them are from
Google, Yahoo, and Bing.
Can search applications be better integrated within Web
information systems? Can they beat generalized search
engines?
Should the SOA community deal with search services?
And then...
– Be concerned with publishing of data sources as search services
– Build tool infrastructures for building search applications
Database Management Prof. Stefano Ceri
77. For those of you asking about WebRatio.... 79
• Company in good shape, over 35 employees, 20%
yearly growth
• New product: BPMN editor, “the most powerful free
BPMN editor”
• Visit: www.webratio.com
Database Management Prof. Stefano Ceri
78. And finally.... 80
问题?
Database Management Prof. Stefano Ceri
Editor's Notes
DATI SITO: 2353 unique visits from JanuaryDal punto di vista dei contenuti, il blog vuole essere un aggregatore di informazioni connesse al Search Computing, in tutte le sue sfaccettature, inclusa quella tecnologica. E’ nostra intenzione, infatti, pubblicare periodicamente tutorial e rassegne riguardanti le tecnologie che vengono utlizzate nello sviluppo del sistema e dei suoi dimostratori. !! DATI BLOG: si può notare un trend di crescita costante nel numero di visitatori. I massimi negativi nei cicli che vediamo corrispondono ai week-end, segno anche del fatto che il blog attira professionisti.