This document summarizes AllegroGraph as a graph database. It discusses AllegroGraph's capabilities as a quintuple store, RDF store, and graph database. It describes AllegroGraph's architecture, extreme use cases including with AMDOCS and the pharmaceutical industry, and includes a demo. Key capabilities highlighted include AllegroGraph's support for property graphs, querying, transactions, indexing, distribution, and languages. Graph algorithms and social network analysis functions using AllegroGraph's generator model are also summarized.
Introduction about how PyPy works.
References:
http://buildbot.pypy.org/misc/antocuni-thesis.pdf
https://bitbucket.org/pypy/extradoc/raw/tip/talk/dls2006/pypy-vm-construction.pdf
http://pypy.readthedocs.org/en/latest/
http://rpython.readthedocs.org/en/latest/
”Everything is a stream“ - This often cited mantra indicates why Reactive Programming is such a powerful tool for handling data flows in almost every part of an application. Reactive Programming has experienced a significant growth in popularity in recent years. But its growing popularity also leads to a Babylonian confusion: the term ”Reactive“ has become overloaded. To understand what Reactive Programming is, this talk surveys the landscape sharpened by trends like Reactive Streams, Reactive Extensions, and Reactive Systems. It then summarizes the basic principles of Reactive Programming by looking at the Reactor library. Finally, it discusses an application of Reactive Programming that lies beyond the standard tutorial examples: an implementation of the BigPipe pattern using Spring 5.
Graphs and Artificial Intelligence have long been a focus for Franz Inc. and currently we are collaborating with Montefiore Health System, Intel, Cloudera, and Cisco to improve a patient’s ability to understand the probabilities of their future health status. By combining artificial intelligence, semantic technologies, big data, graph databases and dynamic visualizations we are deploying a Cognitive Probability Graph concept as a means to help predict future medical events.
The power of Cognitive Probability Graphs stems from the capability to combine the probability space (statistical patient data) with a knowledge base of comprehensive medical codes and a unified terminology system. Cognitive Probability Graphs are remarkable not just because of the possibilities they engender, but also because of their practicality. The confluence of machine learning, semantics, visual querying, graph databases, and big data not only displays links between objects, but also quantifies the probability of their occurrence.
We believe this approach will be transformative for the healthcare field and we see numerous possibilities that exist across business verticals.
During the presentation we will describe the Cognitive Probability Graph concepts using a distributed graph database on top of Hadoop along with the query language SPARQL to extract feature vectors out of the data, applying R and SPARK ML, and then returning the results for further graph processing. #AllegroGraph
InfiniteGraph Presentation from Oct 21, 2010 DBTA WebcastInfiniteGraph
Here is the presentation from Warren Davidson, Director of Business Development, and Darren Wood, InfiniteGraph chief architect. The October 21, 2010 webinar hosted by DBTA, with InfiniteGraph and Riptano, covered new data technologies and how the NOSQL ("Not Only SQL") approach is beneficial in addressing some of the more complex application, scalability and performance requirements in handling vast amounts of data, and in performing advanced analytics on those data volumes with greater ease and speed.
An overview of InfiniteGraph, the distributed graph databaseInfiniteGraph
InfiniteGraph chief architect, Darren Wood, discusses the history, current use cases and future plans for InfiniteGraph, the distributed graph database that helps enterprise and government teams to build applications that find connections and relationships between countless data objects.
Introduction about how PyPy works.
References:
http://buildbot.pypy.org/misc/antocuni-thesis.pdf
https://bitbucket.org/pypy/extradoc/raw/tip/talk/dls2006/pypy-vm-construction.pdf
http://pypy.readthedocs.org/en/latest/
http://rpython.readthedocs.org/en/latest/
”Everything is a stream“ - This often cited mantra indicates why Reactive Programming is such a powerful tool for handling data flows in almost every part of an application. Reactive Programming has experienced a significant growth in popularity in recent years. But its growing popularity also leads to a Babylonian confusion: the term ”Reactive“ has become overloaded. To understand what Reactive Programming is, this talk surveys the landscape sharpened by trends like Reactive Streams, Reactive Extensions, and Reactive Systems. It then summarizes the basic principles of Reactive Programming by looking at the Reactor library. Finally, it discusses an application of Reactive Programming that lies beyond the standard tutorial examples: an implementation of the BigPipe pattern using Spring 5.
Graphs and Artificial Intelligence have long been a focus for Franz Inc. and currently we are collaborating with Montefiore Health System, Intel, Cloudera, and Cisco to improve a patient’s ability to understand the probabilities of their future health status. By combining artificial intelligence, semantic technologies, big data, graph databases and dynamic visualizations we are deploying a Cognitive Probability Graph concept as a means to help predict future medical events.
The power of Cognitive Probability Graphs stems from the capability to combine the probability space (statistical patient data) with a knowledge base of comprehensive medical codes and a unified terminology system. Cognitive Probability Graphs are remarkable not just because of the possibilities they engender, but also because of their practicality. The confluence of machine learning, semantics, visual querying, graph databases, and big data not only displays links between objects, but also quantifies the probability of their occurrence.
We believe this approach will be transformative for the healthcare field and we see numerous possibilities that exist across business verticals.
During the presentation we will describe the Cognitive Probability Graph concepts using a distributed graph database on top of Hadoop along with the query language SPARQL to extract feature vectors out of the data, applying R and SPARK ML, and then returning the results for further graph processing. #AllegroGraph
InfiniteGraph Presentation from Oct 21, 2010 DBTA WebcastInfiniteGraph
Here is the presentation from Warren Davidson, Director of Business Development, and Darren Wood, InfiniteGraph chief architect. The October 21, 2010 webinar hosted by DBTA, with InfiniteGraph and Riptano, covered new data technologies and how the NOSQL ("Not Only SQL") approach is beneficial in addressing some of the more complex application, scalability and performance requirements in handling vast amounts of data, and in performing advanced analytics on those data volumes with greater ease and speed.
An overview of InfiniteGraph, the distributed graph databaseInfiniteGraph
InfiniteGraph chief architect, Darren Wood, discusses the history, current use cases and future plans for InfiniteGraph, the distributed graph database that helps enterprise and government teams to build applications that find connections and relationships between countless data objects.
In this security solution demo, we have integrated Oracle NoSQL DB with InfiniteGraph to demonstrate the power of using the right tools for the solution. By integrating the key value technology of Oracle with the InfiniteGraph distributed graph database, we are able to create new views of existing Call Detail Record (CDR) details to enable discovery of connections, paths and behaviors that may otherwise be missed.
Discover how to add value to your existing Big Data to increase revenues and performance!
Objectivity/DB: A Multipurpose NoSQL DatabaseInfiniteGraph
The speakers will describe the flexible configuration possibilities that Objectivity/DB provides, with an emphasis on how best to distribute data across multiple storage nodes. The session will start by describing the distributed processing architecture of Objectivity/DB before covering the new Placement Manager features. The speakers will also describe how Objectivity/DB compares and contrasts with other NoSQL solutions.
Implementation details of Sparksee's graph database, learn how bitmaps store graph information and how this result in a lightweight & high-performance solution.
Why and how a graph database can serve you better (and at a lower cost) than a relational database when it comes to representing, storing and querying highly interconnected data
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
• Graph theory - origins and concepts
• Benefits of graph databases
• Different types of graph databases
• Typical graph database API
• Programming basics
• Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
• Java programming experience
• Java Developer Kit (JDK)
• Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
• HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
Intro to Graph Databases Using Tinkerpop, TitanDB, and GremlinCaleb Jones
A quick overview of the history, motivation, and uses of graph modeling and graph databases in various industries. Covers a brief introduction to graph databases with an emphasis on the Tinkerpop stack and Gremlin query language. These concepts are then solidified through a hands-on lab modeling a blog engine using Titan and Gremlin.
See more at http://allthingsgraphed.com.
1 Esame Visivo della Struttura nelle NTC 2008 di Aurelio GhersiEugenio Agnello
Slide estratte da una presentazione del prof. Aurelio Ghersi, ordinario di ingegneria strutturale dell’Università di Catania, in occasione di un convegno “Edifici antisismici
in Calcestruzzo Armato, aspetti strutturali e geotecnici secondo le NTC 2008” che si è svolto nel Dicembre 2010 ad Acireale (CT).
Esame visivo della carpenteria e giudizio qualitativo,
Regolarità in pianta ed in Altezza,
Edifici con pareti o nuclei in cemento armato,
Comportamento a mensola e a telaio,
Edifici a struttura intelaiata,
Elementi resistenti alle azioni orizzontali,
Rigidezza,
Individuare gli elementi che resistono alle azioni orizzontali,
Carpenteria: da soli carichi verticali ad azioni orizzontali,
Esempio di analisi edificio civile abitazione,
Esame della carpenteria,
Controllo qualitativo della dimensioni,
Consigli su possibili criteri e dimensionamento dei pilastri,
Giudizio qualitativo sulla struttura.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
In this security solution demo, we have integrated Oracle NoSQL DB with InfiniteGraph to demonstrate the power of using the right tools for the solution. By integrating the key value technology of Oracle with the InfiniteGraph distributed graph database, we are able to create new views of existing Call Detail Record (CDR) details to enable discovery of connections, paths and behaviors that may otherwise be missed.
Discover how to add value to your existing Big Data to increase revenues and performance!
Objectivity/DB: A Multipurpose NoSQL DatabaseInfiniteGraph
The speakers will describe the flexible configuration possibilities that Objectivity/DB provides, with an emphasis on how best to distribute data across multiple storage nodes. The session will start by describing the distributed processing architecture of Objectivity/DB before covering the new Placement Manager features. The speakers will also describe how Objectivity/DB compares and contrasts with other NoSQL solutions.
Implementation details of Sparksee's graph database, learn how bitmaps store graph information and how this result in a lightweight & high-performance solution.
Why and how a graph database can serve you better (and at a lower cost) than a relational database when it comes to representing, storing and querying highly interconnected data
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
• Graph theory - origins and concepts
• Benefits of graph databases
• Different types of graph databases
• Typical graph database API
• Programming basics
• Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
• Java programming experience
• Java Developer Kit (JDK)
• Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
• HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
Intro to Graph Databases Using Tinkerpop, TitanDB, and GremlinCaleb Jones
A quick overview of the history, motivation, and uses of graph modeling and graph databases in various industries. Covers a brief introduction to graph databases with an emphasis on the Tinkerpop stack and Gremlin query language. These concepts are then solidified through a hands-on lab modeling a blog engine using Titan and Gremlin.
See more at http://allthingsgraphed.com.
1 Esame Visivo della Struttura nelle NTC 2008 di Aurelio GhersiEugenio Agnello
Slide estratte da una presentazione del prof. Aurelio Ghersi, ordinario di ingegneria strutturale dell’Università di Catania, in occasione di un convegno “Edifici antisismici
in Calcestruzzo Armato, aspetti strutturali e geotecnici secondo le NTC 2008” che si è svolto nel Dicembre 2010 ad Acireale (CT).
Esame visivo della carpenteria e giudizio qualitativo,
Regolarità in pianta ed in Altezza,
Edifici con pareti o nuclei in cemento armato,
Comportamento a mensola e a telaio,
Edifici a struttura intelaiata,
Elementi resistenti alle azioni orizzontali,
Rigidezza,
Individuare gli elementi che resistono alle azioni orizzontali,
Carpenteria: da soli carichi verticali ad azioni orizzontali,
Esempio di analisi edificio civile abitazione,
Esame della carpenteria,
Controllo qualitativo della dimensioni,
Consigli su possibili criteri e dimensionamento dei pilastri,
Giudizio qualitativo sulla struttura.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Chris Fregly, Research Scientist, PipelineIO at MLconf ATL 2016MLconf
Comparing TensorFlow NLP Options: word2Vec, gloVe, RNN/LSTM, SyntaxNet, and Penn Treebank: Through code samples and demos, we’ll compare the architectures and algorithms of the various TensorFlow NLP options. We’ll explore both feed-forward and recurrent neural networks such as word2vec, gloVe, RNN/LSTM, SyntaxNet, and Penn Treebank using the latest TensorFlow libraries.
Making NumPy-style and Pandas-style code faster and run in parallel. Continuum has been working on scaled versions of NumPy and Pandas for 4 years. This talk describes how Numba and Dask provide scaled Python today.
Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen wide spread use. Why has this been the case?
In this presentation, we will first introduce RNNs as a concept. Then we will sketch how to implement them and cover the tricks necessary to make them work well. With the basics covered, we will investigate using RNNs as general text classification and regression models, examining where they succeed and where they fail compared to more traditional text analysis models. A straightforward open-source Python and Theano library for training RNNs with a scikit-learn style interface will be introduced and we’ll see how to use it through a tutorial on a real world text dataset
Applying graph analytics on data stored in relational databases can provide tremendous value in many application domains. We discuss the importance of leveraging these analyses, and the challenges in enabling them. We present a tool, called GraphGen, that allows users to visually explore, and rapidly analyze (using NetworkX) different graph structures present in their databases.
Sjug #26 ml is in java but is dl too - ver1.04 - tomasz sikora 2018-03-23Tomasz Sikora
Vint Cerf, recognised as one of "the fathers of the Internet" as co-inventor of TCP/IP, said "And programming computers was so fascinating. You create your own little universe, and then it does what you tell it to do.". Now when computers are learning these words give different ground to debate on developer context here.
So what can we do from old good Software Craftsmanship perspective? I, as a developer who still believes in Java, can I use my beloved platform? How can I tackle problems requiring deeper models? On this session Tomasz tried to answer those, opening 100s more questions ;). He executed and configured a few models trying to explore the field from as practical perspective as possible.
To get more visit https://twitter.com/tomaszsikora and http://silesia.jug.pl/
Standardizing on a single N-dimensional array API for PythonRalf Gommers
MXNet workshop Dec 2020 presentation on the array API standardization effort ongoing in the Consortium for Python Data API Standards - see data-apis.org
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
Watching Pigs Fly with the Netflix Hadoop Toolkit (Hadoop Summit 2013)Jeff Magnusson
Overview of the data platform as a service architecture at Netflix. We examine the tools and services built around the Netflix Hadoop platform that are designed to make access to big data at Netflix easy, efficient, and self-service for our users.
From the perspective of a user of the platform, we walk through how various services in the architecture can be used to build a recommendation engine. Sting, a tool for fast in memory aggregation and data visualization, and Lipstick, our workflow visualization and monitoring tool for Apache Pig, are discussed in depth. Lipstick is now part of Netflix OSS - clone it on github, or learn more from our techblog post: http://techblog.netflix.com/2013/06/introducing-lipstick-on-apache-pig.html.
The Rise of the DataOps - Dataiku - J On the Beach 2016 Dataiku
Many organisations are creating groups dedicated to data. These groups have many names : Data Team, Data Labs, Analytics Teams….
But whatever the name, the success of those teams depends a lot on the quality of the data infrastructure and their ability to actually deploy data science applications in production.
In that regards a new role of “DataOps” is emerging. Similar, to Dev Ops for (Web) Dev, the Data Ops is a merge between a data engineer and a platform administrator. Well versed in cluster administration and optimisation, a data ops would have also a perspective on the quality of data quality and the relevance of predictive models.
Do you want to be a Data Ops ? We’ll discuss its role and challenges during this talk
Donald Miner will do a quick introduction to Apache Hadoop, then discuss the different ways Python can be used to get the job done in Hadoop. This includes writing MapReduce jobs in Python in various different ways, interacting with HBase, writing custom behavior in Pig and Hive, interacting with the Hadoop Distributed File System, using Spark, and integration with other corners of the Hadoop ecosystem. The state of Python with Hadoop is far from stable, so we'll spend some honest time talking about the state of these open source projects and what's missing will also be discussed.
"Introducing Distributed Tracing in a Large Software System", Kostiantyn Sha...Fwdays
Software systems are growing in size and complexity when the business is growing, and sometimes it is hard to figure out what is going on. Various teams make different changes for different business capabilities. Distributed Tracing is a useful way to look under the hood and see for yourself what operations are being performed, what services are used in a certain use case, and how performant are they. In this talk, I will present what Distributed Tracing is and how we introduced it into our software system with some tips and tricks on what you should focus on if you want to do the same.
This paper presents an extensive experimental study of the state-of-the-art of XML compression tools. The study reports the behavior of nine XML compressors using a large corpus of XML documents which covers the dierent natures and scales of XML documents. In addition to assessing and comparing the performance characteristics of the evaluated XML compression tools, the study tries to assess the effectiveness and practicality of using these tools in the real world. Finally, we provide some guidelines and recommendations which are useful for helping developers and users for making an effective decision for selecting the most suitable XML compression tool for their needs.
1. AllegroGraph as a Graph Database JansAasman, Ph.D. CEO - Franz Inc Ja@Franz.com
2.
3. Contents AllegroGraph as a QuintupleStore (well OcttupleStore in 2011) RDF store Graph Database Agraph architecture Extreme use cases AMDOCS … CRM on top a trillion triples Pharmaceutic … explore connections in graph space Demo
4. Agraph as a quintuple store S, P, O, G + unique ID + transaction # SPOG can be any data type 1 2.0 3 4 2001-12-12 after 010-12-12 +19258781444 Jans loves pizza file1 12 NoOne believes 12 And include very efficient geospatial and temporal representations and indices 6 default indices, 24 user controlled indices Range indexing, Freetext Indexing Neighborhood matrixes & UPI maps (for 1 ms access) 2011: time, security
5. Agraph as an RDF store RDF store when you adhere to the RDF conventions. Full Sparql 1.0, most of Sparql 1.1 RDFS++ reasoner GeoSpatial and Temporal representations. Prolog for Rules Soon Common Logic (CLIF+) As a usability layer on top of Prolog Easier to combine Rules and Queries
6. Agraph as a Graph Database If you want a Property Graph: use the graph argument Jans loves pizza gr1 gr1 weight 90 gr1 author Sophia
39. Many graph algorithms using generator model Because of Social Network Analysis requirements we implement many graph algorithms. Using generators A first class function that takes One node as input Returns all children And neighbourhood matrices(or adjacency hash-tables) forspeed.
40. how far is Actor1 from Actor2? Degrees of separation How far is P1 from P2 Connection strength How many shortest paths from P1 to P2 through a series of predicates and rules
41. In what groups is this actor? Find the ego-network around a person or thing Friend, friends of friends, etc. Find all the fully connect graphs around a personor thing
42. Questions in SNA: How Important is an actor? In-degree, out-degree Actor degree centrality I have the most connections in a group so I am more important Actor closeness centrality I have more shortest paths to anyone else in the group so I am more important Actor betweenness centrality I am more often on the shortest path between other people in the group so I am more important. I can control flow of information better than other people
43. Has the group a leader, is the group cohesive? Group centralization How centralized is this group? Does this group have a leader Is there someone controllingthe information flow Group cohesiveness How strong and well connected is this group Are most people connected What is the density
44. All search and SNA functions use Generators Generator Input: one node Output: list of nodes Fully functional, can be complex sparql or prolog queries Or just predicates and indication of direction
45. How to get from A to E?? subjpredobj a dinner-with b a kissed-with c c movie-with e b kissed-with d d movie-with e e dinner-with a (defgenerator knows (node) (objects-of :p dinner-with)) (defgenerator knows (node) (objects-of :p dinner-with) (subjects-of :p dinner-with))
46. How to get from A to E?? (defgenerator knows () (object-of :p dinner-with) (subject-of :p dinner-with) (object-of :p movie-with) (subject-of :p movie-with) (object-of :p kissed-with) (subject-of :p kissed-with)) (defgenerator knows () (undirected (dinner-with movie-with kissed-with)))
48. Sample SNA functions (Ego-group actor generator depth ?group) - binds ?group to group of nodes (Ego-group-members actor generator depth ?a) - bind ?a to every member in the group (Cliques actor generator min-depth ?cl) - binds ?cl to all cliques (Clique-members actor generator min-depth ?cl ?a) - binds ?cl to cliques and then iterates of ever member ?a in ?cl (Actor-centrality actor group generator ?num) - binds ?num to actorcentrality (Actor-centrality-members group ?actor ?num) - binds ?actor to every actor in group, ?centrality is centrality of that actor, we start with the actor with highest centrality. (Group-centrality group generator ?num) Actor = single node Group = list of nodes Depth = number Generator = generator
50. Where we use this? Amdocs: Know everything about every customer Partitioned on customer Most graph search centered in client Pfizer: help me find connections between drugs, diseases, genes, side effects in a sea of clinical trials Just a mess of data All graph search in server
52. Can you in < 1 second with one push of a button Predict the three most likely reasons why Joe Smith from Kansas is calling the call center? Bill unexpectedly high, loosing connection too often, doesn’t know how to use new subscription service? The ten last events that happened for JS? Phone calls, sms, downloads of movie, device stopped working, payment of bill, looking at map, search for local store. What is the likelyhoodthat he will change from T-Mobile to Sprint or AT&T? What are his ten most important friends and what devices do they have. And who is the first to change and who follows?
53. Can you in < 1 second with one push of a button What are the usual daily locations for this person? What kind of shops? What kind of services does he download, what kind of movies/music/games does he like, what products does he buy? Is his plan the right plan for him? Is he in a good mood? Is he a valuable customer, is he a good payer, what is your margin on him, how many times per month does he call a call center, does he look up help for mail on the internet? Can you predict if he is going to pay the bill?
54.
55. Architecture Decision Engine Actions Events SBA Application Server Container Amdocs Event Collector Container Inference Engine(Business Rules) Amdocs Integration Framework Event Ingestion Events Scheduled Events Bayesian Belief Network CRM RM OMS CRM “Sesame” Operational Systems NW Web 2.0 AllegroGraph Triple Store DB Event Data Sources