Utrecht NL-HUG/Data Science-NL - Agile Data Slides

Russell Jurney (@rjurney) - Hadoop Evangelist @Hortonworks

Formerly Viz, Data Science at Ning, LinkedIn

HBase Dashboards, Career Explorer, InMaps

Agile Analytics Applications

Agile Data - The Book (March, 2013)

Read it now on OFPS

A philosophy,not the only way

But still, its good! Really!

We go fast... but don’t worry!

• Examples for EVERYTHING on the Hortonworks blog: http://hortonworks.com/blog/authors/russell_jurney

• Download the slides - click the links - read examples!

• If its not on the blog, its in the book!

• Order now: http://shop.oreilly.com/product/0636920025054.do

• Read the book NOW on OFPS: • http://ofps.oreilly.com/titles/9781449326265/chapter_2.html

Agile Application Development: Check

• LAMP stack mature• Post-Rails frameworks to choose from• Enable rapid feedback and agility

+ NoSQL

Data Warehousing

Scientific Computing / HPC

• ‘Smart kid’ only: MPI, Globus, etc. until Hadoop

Tubes and Mercury (old school) Cores and Spindles (new school)

UNIVAC and Deep Blue both fill a warehouse. We’re back...

Data Science?

ApplicationDevelopment Data Warehousing

Scientific Computing / HPC

Data Center as Computer

• Warehouse Scale Computers and applications

“A key challenge for architects of WSCs is to smooth out these discrepancies in a cost efficient manner.” Click here for a paper on operating a ‘data center as computer.’

Hadoop to the Rescue!

MPP EDW NewSQL

SQL NoSQL NewSQL

Big data refinery / Modernize ETL

Business Transactions & Interactions

Web, Mobile, CRM, ERP, SCM, …

Business Intelligence & Analytics

Dashboards, Reports, Visualization, …

Audio, Video, Images

Docs, Text, XML

Web Logs, Clicks

Social, Graph, Feeds

Sensors, Devices,

Spatial, GPS

Events, Other

New Data Sources

Big Data Refinery

Apache Hadoop

I stole this slide from Eric. Update: He stole it from someone else.

Hadoop to the Rescue!

• Easy to use! (Pig, Hive, Cascading)

• CHEAP: 1% the cost of SAN/NAS

• A department can afford its own Hadoop cluster!

• Dump all your data in one place: Hadoop DFS

• Silos come CRASHING DOWN!

• JOIN like crazy!

• ETL like whoah!

• An army of mappers and reducers at your command

• OMGWTFBBQ ITS SO GREAT! I FEEL AWESOME!10

NOW WHAT?

Analytics Apps: It takes a Team

• Broad skill-set to make useful apps

• Basically nobody has them all

• Application development is inherently collaborative

Data Science Team

• 3-4 team members with broad, diverse skill-sets that overlap• Transactional overhead dominates at 5+ people

• Expert researchers: lend 25-50% of their time to teams• Pick relevant researchers. Leave them alone. They’ll spawn new products by accident. Not just CS/Math. Design. Art?

• Creative workers. Run like a studio, not an assembly line• Total freedom... with goals and deliverables.

• Work environment matters most: private, social & quiet space• Desks/cubes optional

How to get insight into product?

• Back-end has gotten t-h-i-c-k-e-r

• Generating $$$ insight can take 10-100x app dev

• Timeline disjoint: analytics vs agile app-dev/design

• How do you ship insights efficiently?

• How do you collaborate on research vs developer timeline?14

The Wrong Way - Part One

“We made a great design. Your job is to predict the future for it.”

The Wrong Way - Part Two

“Whats taking you so long to reliably predict the future?”

The Wrong Way - Part Three

“The users don’t understand what 86% true means.”

The Wrong Way - Part Four

GHJIAEHGIEhjagigehganbanbigaebjnain!!!!!RJ(@J?!!

The Wrong Way - Inevitable Conclusion

Plane Mountain

Reminds me of... the waterfall model

Chief Problem

You can’t design insight in analytics applications.

You discover it.

You discover by exploring.

-> Strategy

So make an app for exploring your data.

Which becomes a palette for what you ship.

Iterate and publish intermediate results.

Data Design

• Not the 1st query that = insight, its the 15th, or the 150th

• Capturing “Ah ha!” moments

• Slow to do those in batch...

• Faster, better context in an interactive web application.

• Pre-designed charts wind up terrible. So bad.

• Easy to invest man-years in the wrong statistical models

• Semantics of presenting predictions are complex, delicate

• Opportunity lies at intersection of data & design

How do we get back to Agile?

Statement of Principles

(then tricks, with code)

Setup an environment where...

• Insights repeatedly produced

• Iterative work shared with entire team

• Interactive from day 0

• Data model is consistent end-to-end

• Minimal impedance between layers

• Scope and depth of insights grow

• Insights form the palette for what you ship

• Until the application pays for itself and more

Value document > relation

Most data is dirty. Most data is semi-structured or un-structured. Rejoice!

Value document > relation

Note: Hive/ArrayQL/NewSQL’s support of documents/array types blur this distinction.

Relational Data = Legacy?

• Why JOIN? Storage is fundamentally cheap!

• Duplicate that JOIN data in one big record type!

• ETL once to document format on import, NOT every job

• Not zero JOINs, but far fewer JOINs

• Semi-structured documents preserve data’s actual structure

• Column compressed document formats beat JOINs! (paper coming)

Value imperative > declarative

• We don’t know what we want to SELECT.

• Data is dirty - check each step, clean iteratively.

• 85% of data scientist’s time spent munging. See: ETL.

• Imperative is optimized for our process.

• Process = iterative, snowballing insight

• Efficiency matters, self optimize

Value dataflow > SELECT

Ex. dataflow: ETL + email sent count

32(I can’t read this either. Get a big version here.)

Value Pig > Hive (for app-dev)

• Pigs eat ANYTHING• Pig is optimized for refining data, as opposed to consuming it• Pig is imperative, iterative• Pig is dataflows, and SQLish (but not SQL)• Code modularization/re-use: Pig Macros• ILLUSTRATE speeds dev time (even UDFs)• Easy UDFs in Java, JRuby, Jython, Javascript• Pig Streaming = use any tool, period.• Easily prepare our data as it will appear in our app.• If you prefer Hive, use Hive.

But actually, I wish Pig and Hive were one tool. Pig, then Hive, then Pig, then Hive... See: HCatalog for Pig/Hive integration, and this post.

Localhost vs Petabyte scale: same tools

• Simplicity essential to scalability: highest level tools we can• Prepare a good sample - tricky with joins, easy with documents

• Local mode: pig -l /tmp -x local -v -w• Frequent use of ILLUSTRATE• 1st: Iterate, debug & publish locally• 2nd: Run on cluster, publish to team/customer• Consider skipping Object-Relational-Mapping (ORM)• We do not trust ‘databases,’ only HDFS @ n=3.

• Everything we serve in our app is re-creatable via Hadoop.

Data-Value Pyramid

Climb it. Do not skip steps. See here.

0/1) Display atomic records on the web

0.0) Document-serialize events

• Protobuf

• Thrift

• JSON

• Avro - I use Avro because the schema is onboard.

0.1) Documents via Relation ETL

enron_messages = load '/enron/enron_messages.tsv' as (

message_id:chararray,

sql_date:chararray,

from_address:chararray,

from_name:chararray,

subject:chararray,

body:chararray

enron_recipients = load '/enron/enron_recipients.tsv' as ( message_id:chararray, reciptype:chararray, address:chararray, name:chararray);

split enron_recipients into tos IF reciptype=='to', ccs IF reciptype=='cc', bccs IF reciptype=='bcc';

headers = cogroup tos by message_id, ccs by message_id, bccs by message_id parallel 10;

with_headers = join headers by group, enron_messages by message_id parallel 10;

emails = foreach with_headers generate enron_messages::message_id as message_id,

CustomFormatToISO(enron_messages::sql_date, 'yyyy-MM-dd HH:mm:ss') as date,

TOTUPLE(enron_messages::from_address, enron_messages::from_name) as from:tuple(address:chararray, name:chararray),

enron_messages::subject as subject,

enron_messages::body as body,

headers::tos.(address, name) as tos,

headers::ccs.(address, name) as ccs,

headers::bccs.(address, name) as bccs;

store emails into '/enron/emails.avro' using AvroStorage(

Example here.

0.2) Serialize events from streams

class GmailSlurper(object): ... def init_imap(self, username, password): self.username = username self.password = password try: imap.shutdown() except: pass self.imap = imaplib.IMAP4_SSL('imap.gmail.com', 993) self.imap.login(username, password) self.imap.is_readonly = True ... def write(self, record): self.avro_writer.append(record) ... def slurp(self): if(self.imap and self.imap_folder): for email_id in self.id_list: (status, email_hash, charset) = self.fetch_email(email_id) if(status == 'OK' and charset and 'thread_id' in email_hash and 'froms' in email_hash): print email_id, charset, email_hash['thread_id'] self.write(email_hash)

Scrape your own gmail in Python and Ruby.

0.3) ETL Logs

log_data = LOAD 'access_log' USING org.apache.pig.piggybank.storage.apachelog.CommongLogLoader AS (remoteAddr, remoteLogname, user, time, method, uri, proto, bytes);

1) Plumb atomic events -> browser

(Example stack that enables high productivity)

Lots of Stack Options with Examples

• Pig with Voldemort, Ruby, Sinatra: example

• Pig with ElasticSearch: example

• Pig with MongoDB, Node.js: example

• Pig with Cassandra, Python Streaming, Flask: example

• Pig with HBase, JRuby, Sinatra: example

• Pig with Hive via HCatalog: example (trivial on HDP)

• Up next: Accumulo, Redis, MySQL, etc.

1.1) cat our Avro serialized events

me$ cat_avro ~/Data/enron.avro{ u'bccs': [], u'body': u'scamming people, blah blah', u'ccs': [], u'date': u'2000-08-28T01:50:00.000Z', u'from': {u'address': u'bob.dobbs@enron.com', u'name': None}, u'message_id': u'<1731.10095812390082.JavaMail.evans@thyme>', u'subject': u'Re: Enron trade for frop futures', u'tos': [ {u'address': u'connie@enron.com', u'name': None} ]}

Get cat_avro in python, ruby

1.2) Load our events in Pig

me$ pig -l /tmp -x local -v -wgrunt> enron_emails = LOAD '/enron/emails.avro' USING AvroStorage();grunt> describe enron_emails

emails: { message_id: chararray, datetime: chararray, from:tuple(address:chararray,name:chararray) subject: chararray, body: chararray, tos: {to: (address: chararray,name: chararray)}, ccs: {cc: (address: chararray,name: chararray)}, bccs: {bcc: (address: chararray,name: chararray)}}

1.3) ILLUSTRATE our events in Pig

grunt> illustrate enron_emails

---------------------------------------------------------------------------| emails | | message_id:chararray | | datetime:chararray | | from:tuple(address:chararray,name:chararray) | | subject:chararray | | body:chararray | | tos:bag{to:tuple(address:chararray,name:chararray)} || ccs:bag{cc:tuple(address:chararray,name:chararray)} || bccs:bag{bcc:tuple(address:chararray,name:chararray)} |---------------------------------------------------------------------------| | | <1731.10095812390082.JavaMail.evans@thyme> | | 2001-01-09T06:38:00.000Z | | (bob.dobbs@enron.com, J.R. Bob Dobbs) | | Re: Enron trade for frop futures | | scamming people, blah blah | | {(connie@enron.com,)} | | {} | | {} |

Upgrade to Pig 0.10+

1.4) Publish our events to a ‘database’

pig -l /tmp -x local -v -w -param avros=enron.avro \ -param mongourl='mongodb://localhost/enron.emails' avro_to_mongo.pig

/* MongoDB libraries and configuration */register /me/mongo-hadoop/mongo-2.7.3.jarregister /me/mongo-hadoop/core/target/mongo-hadoop-core-1.1.0-SNAPSHOT.jarregister /me/mongo-hadoop/pig/target/mongo-hadoop-pig-1.1.0-SNAPSHOT.jar

/* Set speculative execution off to avoid chance of duplicate records in Mongo */set mapred.map.tasks.speculative.execution falseset mapred.reduce.tasks.speculative.execution falsedefine MongoStorage com.mongodb.hadoop.pig.MongoStorage(); /* Shortcut */

/* By default, lets have 5 reducers */set default_parallel 5

avros = load '$avros' using AvroStorage();store avros into '$mongourl' using MongoStorage();

Full instructions here.

Which does this:

From Avro to MongoDB in one command:

1.5) Check events in our ‘database’

$ mongo enron

MongoDB shell version: 2.0.2connecting to: enron

> show collectionsemailssystem.indexes

> db.emails.findOne({message_id: "<1731.10095812390082.JavaMail.evans@thyme>"}){" "_id" : ObjectId("502b4ae703643a6a49c8d180")," "message_id" : "<1731.10095812390082.JavaMail.evans@thyme>"," "date" : "2001-01-09T06:38:00.000Z"," "from" : { "address" : "bob.dobbs@enron.com", "name" : "J.R. Bob Dobbs" }," "subject" : Re: Enron trade for frop futures," "body" : "Scamming more people..."," "tos" : [ { "address" : "connie@enron", "name" : null } ]," "ccs" : [ ]," "bccs" : [ ]}

1.6) Publish events on the web

require 'rubygems'require 'sinatra'require 'mongo'require 'json'

connection = Mongo::Connection.newdatabase = connection['agile_data']collection = database['emails']

get '/email/:message_id' do |message_id| data = collection.find_one({:message_id => message_id}) JSON.generate(data)end

1.6) Publish events on the web

Whats the point?

• A designer can work against real data.

• An application developer can work against real data.

• A product manager can think in terms of real data.

• Entire team is grounded in reality!

• You’ll see how ugly your data really is.

• You’ll see how much work you have yet to do.

• Ship early and often!

• Feels agile, don’t it? Keep it up!

1.7) Wrap events with Bootstrap

</head>

<body>

<thead>

{% for key in data['keys'] %}

{% endfor %}

</thead>

<tbody>

{% for value in data['values'] %}

<td>{{ value }}</td>

{% endfor %}

</tbody>

</table>

</div>

</body> Complete example here with code here.

1.7) Wrap events with Bootstrap

Refine. Add links between documents.

53Not the Mona Lisa, but coming along... See: here

1.8) List links to sorted events

mongo enron

> db.emails.ensureIndex({message_id: 1})

> db.emails.find().sort({date:0}).limit(10).pretty()

" "_id" : ObjectId("4f7a5da2414e4dd0645d1176"),

" "message_id" : "<CA+bvURyn-rLcH_JXeuzhyq8T9RNq+YJ_Hkvhnrpk8zfYshL-wA@mail.gmail.com>",

" "from" : [

pig -l /tmp -x local -v -w

emails_per_user = foreach (group emails by from.address) {

sorted = order emails by date;

last_1000 = limit sorted 1000;

generate group as from_address, emails as emails;

store emails_per_user into '$mongourl' using MongoStorage();

Use Pig, serve/cache a bag/array of email documents:

Use your ‘database’, if it can sort.

1.8) List links to sorted documents

1.9) Make it searchable...

If you have list, search is easy with ElasticSearch and Wonderdog...

/* Load ElasticSearch integration */

register '/me/wonderdog/target/wonderdog-1.0-SNAPSHOT.jar';

register '/me/elasticsearch-0.18.6/lib/*';

define ElasticSearch com.infochimps.elasticsearch.pig.ElasticSearchStorage();

emails = load '/me/tmp/emails' using AvroStorage();

store emails into 'es://email/email?json=false&size=1000' using ElasticSearch('/me/

elasticsearch-0.18.6/config/elasticsearch.yml', '/me/elasticsearch-0.18.6/plugins');

curl -XGET 'http://localhost:9200/email/email/_search?q=hadoop&pretty=true&size=1'

Test it with curl:

ElasticSearch has no security features. Take note. Isolate.

From now on we speed up...

Don’t worry, its in the book and on the blog.

http://hortonworks.com/blog/

2) Create Simple Charts

2) Create Simple Tables and Charts

2) Create Simple Charts

• Start with an HTML table on general principle.

• Then use nvd3.js - reusable charts for d3.js

• Aggregate by properties & displaying is first step in entity

resolution

• Start extracting entities. Ex: people, places, topics, time series

• Group documents by entities, rank and count.

• Publish top N, time series, etc.

• Fill a page with charts.

• Add a chart to your event page.

2.1) Top N (of anything) in Pig

top_things = foreach (group things by key) { sorted = order things by arbitrary_rank desc;top_10_things = limit sorted 10;generate group as key, top_10_things as top_10_things;};

store top_n into '$mongourl' using MongoStorage();

Remember, this is the same structure the browser gets as json.

This would make a good Pig Macro.

2.2) Time Series (of anything) in Pig

/* Group by our key and date rounded to the month, get a total */things_by_month = foreach (group things by (key, ISOToMonth(datetime))

generate flatten(group) as (key, month), COUNT_STAR(things) as total;

/* Sort our totals per key by month to get a time series */things_timeseries = foreach (group things_by_month by key) {

timeseries = order things by month;generate group as key, timeseries as timeseries;};

store things_timeseries into '$mongourl' using MongoStorage();

Yet another good Pig Macro.

Data processing in our stack

A new feature in our application might begin at any layer... great!

Any team member can add new features, no problemo!

I’m creative!I know Pig!

I’m creative too!I <3 Javascript!

omghi2u!where r my legs?

send halp

Data processing in our stack

... but we shift the data-processing towards batch, as we are able.

Ex: Overall total emails calculated in each layer

See real example here.

3) Exploring with Reports

3.0) From charts to reports...

• Extract entities from properties we aggregated by in charts (Step 2)

• Each entity gets its own type of web page

• Each unique entity gets its own web page

• Link to entities as they appear in atomic event documents (Step 1)

• Link most related entities together, same and between types.

• More visualizations!

• Parametize results via forms.

3.1) Looks like this...

3.2) Cultivate common keyspaces

3.3) Get people clicking. Learn.

• Explore this web of generated pages, charts and links!

• Everyone on the team gets to know your data.

• Keep trying out different charts, metrics, entities, links.

• See whats interesting.

• Figure out what data needs cleaning and clean it.

• Start thinking about predictions & recommendations.

‘People’ could be just your team, if data is sensitive.

4) Predictions and Recommendations

4.0) Preparation

• We’ve already extracted entities, their properties and relationships

• Our charts show where our signal is rich

• We’ve cleaned our data to make it presentable

• The entire team has an intuitive understanding of the data

• They got that understanding by exploring the data

• We are all on the same page!

4.2) Think in different perspectives

• Networks

• Time Series / Distributions

• Natural Language Processing

• Conditional Probabilities / Bayesian Inference

• Check out Chapter 2 of the book...

73See here.

4.3) Networks

4.3.1) Weighted Email Networks in Pig

DEFINE header_pairs(email, col1, col2) RETURNS pairs { filtered = FILTER $email BY ($col1 IS NOT NULL) AND ($col2 IS NOT NULL); flat = FOREACH filtered GENERATE FLATTEN($col1) AS $col1, FLATTEN($col2) AS $col2; $pairs = FOREACH flat GENERATE LOWER($col1) AS ego1, LOWER($col2) AS ego2;}

/* Get email address pairs for each type of connection, and union them together */emails = LOAD '/me/Data/enron.avro' USING AvroStorage();from_to = header_pairs(emails, from, to);from_cc = header_pairs(emails, from, cc);from_bcc = header_pairs(emails, from, bcc);pairs = UNION from_to, from_cc, from_bcc;

/* Get a count of emails over these edges. */pair_groups = GROUP pairs BY (ego1, ego2);sent_counts = FOREACH pair_groups GENERATE FLATTEN(group) AS (ego1, ego2), COUNT_STAR(pairs) AS total;

4.3.2) Networks Viz with Gephi

4.3.3) Gephi = Easy

4.3.4) Social Network Analysis

4.4) Time Series & Distributions

/* Count things per day */

things_per_day = foreach (group things by (key, ISOToDay(datetime))

generate flatten(group) as (key, day),

COUNT_STAR(things) as total;

/* Sort our totals per key by day to get a sorted time series */

things_timeseries = foreach (group things_by_day by key) {

timeseries = order things by day;

generate group as key, timeseries as timeseries;

store things_timeseries into '$mongourl' using MongoStorage();

4.4.1) Smooth Sparse Data

80See here.

4.4.2) Regress to find Trends

JRuby Linear Regression UDF Pig to use the UDF

Trend Line in your Application

4.5.1) Natural Language Processing

82Example with code here and macro here.

import 'tfidf.macro';my_tf_idf_scores = tf_idf(id_body, 'message_id', 'body');

/* Get the top 10 Tf*Idf scores per message */per_message_cassandra = foreach (group tfidf_all by message_id) { sorted = order tfidf_all by value desc; top_10_topics = limit sorted 10; generate group, top_10_topics.(score, value);}

4.5.2) NLP: Extract Topics!

4.5.3) NLP for All: Extract Topics!

• TF-IDF in Pig - 2 lines of code with Pig Macros: • http://hortonworks.com/blog/pig-macro-for-tf-idf-makes-topic-summarization-2-lines-of-pig/

• LDA with Pig and the Lucene Tokenizer: • http://thedatachef.blogspot.be/2012/03/topic-discovery-with-apache-pig-and.html

4.6) Probability & Bayesian Inference

4.6.1) Gmail Suggested Recipients

4.6.1) Reproducing it with Pig...

4.6.2) Step 1: COUNT(From -> To)

4.6.2) Step 2: COUNT(From, To, Cc)/Total

P(cc | to) = Probability of cc’ing someone, given that you’ve to’d someone

4.6.3) Wait - Stop Here! It works!

They match...

4.4) Add predictions to reports

5) Enable new actions

Example: Packetpig and PacketLoop

snort_alerts = LOAD '$pcap' USING com.packetloop.packetpig.loaders.pcap.detection.SnortLoader('$snortconfig');

countries = FOREACH snort_alerts GENERATE com.packetloop.packetpig.udf.geoip.Country(src) as country, priority;

countries = GROUP countries BY country;

countries = FOREACH countries GENERATE group, AVG(countries.priority) as average_severity;

STORE countries into 'output/choropleth_countries' using PigStorage(',');

Code here.

Example: Packetpig and PacketLoop

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Thank You!

Questions & Answers

Slides: http://slidesha.re/O8kjaF

Follow: @hortonworks and @rjurneyRead: hortonworks.com/blog

Utrecht NL-HUG/Data Science-NL - Agile Data Slides

Technology

Process Intensification @ PIN NL · Sulzer Chemtech PIN NL Meeting | November 10th 2010 | Utrecht Title: Process Intensification In Real Life Jeffrey FELIX, | Sulzer Chemtech Ltd

Ronald van Oeveren ECOMM 2015 NL-Utrecht The True Value of Mobility Management

Doing Television History Outside the Box: Unexplored Territories on the European Agenda - Dana Mustata (Utrecht University, NL)

EuroTAAC Utrecht, NL 24.02.2000 Tamer Çatalkaya Abt. E.I.S. TU BS The Home-Made PageHistory List Tamer Çatalkaya TU Braunschweig Department of Integrated

Sjaak Brinkkemper MBI Colloquium - Utrecht University · Giarte. 104: NL, 2003. 25% positive ROI: Forrester. 111: US, 2003. 75% ‘somewhat satisfied

LTI Update at the IMS QUarterly Meeting, Utrecht, NL

Introduction NL-HUG (April)

Meters Maken voor Millennium (Making Miles for Millennium) and OLPC NL Harrie Vollaard en Gertie Clabbers 13 december 2008 Utrecht

A Worldwide e-Infrastucture for NMR and structural biology Project Coordinator: Prof. Alexandre M.J.J. Bonvin, Utrecht University, NL a.m.j.j.bonvin@uu.nl

First NL-HUG: Large-scale data processing at SARA with Apache Hadoop

aperture screen - Universiteit Utrecht · 2003-09-15 · APERTURE SCREENS FOR SOLAR TELESCOPES Robert H. Harmerschlag Zonnenburg 2, 3512 NL Utrecht, The Netherlands ABSTRACT A screen

Bert Niesters - NIBSC - Home · 2011-09-13 · Rob Schuurman UMCU, Utrecht, NL Bert Niesters UMCG, Groningen, NL. International Standards ... C Inf AB RSV Rhino Para 1/3 Para 2/4

Utrecht celebrates peace - Vrede van Utrechtvredevanutrecht2013.nl/media/files/vvu052-61... · The˛Treaty of Utrecht marks a turning point in world history. After eighteen months

Structural Biology on the GRID Dr. Tsjerk A. Wassenaar Biomolecular NMR - Utrecht University (NL) tsjerkw@gmail.com

HUG Bigtop

CLARIN-NL First Call Jan Odijk CLARIN-NL Kick-off Meeting Utrecht, 27 May 2009

CLARIN: Goals and Structure of the Project Steven Krauwer CLARIN Coordinator Utrecht institute of Linguistics UiL-OTS (NL)

Hug Geoconsult

London hug

A BEGINNER’S GUIDE TO ADAPTIVE DYNAMICS...A BEGINNER’S GUIDE TO ADAPTIVE DYNAMICS ODO DIEKMANN Mathematisch Instituut, Universiteit Utrecht P.O. Box 80010, NL 3508 TA Utrecht,