BBVA Data & Analytics was established in 2014 as a new data science center to extract value from BBVA's data through developing data engines, data-based products and services, and data-based consulting projects. The team analyzes card payment data to describe socio-economic activity patterns and measure the permeability and attractiveness of cities. Their research has resulted in over 15 scientific papers and visualizations that provide insights into economic and mobility patterns. Current projects include analyzing tourism patterns in Mexico and developing predictive models of regional economic indices.
Smart tourism destinations:The pillars of their intelligenceJavier Blanco
Los "destinos turísticos inteligentes" es un término emergente para una realidad todavía insuficientemente definida. Lo cierto es que la era digital ofrece nuevas y amplias posibilidades para progresar y es una oportunidad para introducir nuevas dinámicas y nuevos contextos. La información y la calidad del órgano gestor del destino adquirirán especial relevancia.
La presentazione di Aurkene Alzua Sorzabal, direttrice di CICtourGUNE, sui nuovi modelli di business turistici - WEF è internazionalizzazione e cosmopolitismo (23 marzo 2014)
Smart tourism destinations:The pillars of their intelligenceJavier Blanco
Los "destinos turísticos inteligentes" es un término emergente para una realidad todavía insuficientemente definida. Lo cierto es que la era digital ofrece nuevas y amplias posibilidades para progresar y es una oportunidad para introducir nuevas dinámicas y nuevos contextos. La información y la calidad del órgano gestor del destino adquirirán especial relevancia.
La presentazione di Aurkene Alzua Sorzabal, direttrice di CICtourGUNE, sui nuovi modelli di business turistici - WEF è internazionalizzazione e cosmopolitismo (23 marzo 2014)
Smart tourism is an upcoming and novel exhortation applied to portray the growing reliance of tourism destinations globally. The tourism industry and its consumers (tourists) are emerging forms of information and communications technology (ICT) that permit for substantial quota of information in the form of data to be modified into value propositions. Nevertheless, it remains vague concept, which hampers its theoretical development. The efforts in this study are put together for defining smart tourism, and the research sheds light on present trends in smart tourism, and then laying out its business and technological establishment. This is pursued by a concise dialogue on the scenario and limitations of smart tourism. The research further draws attention to the immense call for investigation to enlighten smart tourism management and development in present scenario.
Guest lecture, delivered to masters students at Università Ca' Foscari Venezia, Italy, covering digital marketing, social media, mobile and e-learning technologies. Prepared and delivered April 2011.
This study aimed to investigate the impact of mobile phone, land phone and internet (ICTs) on sales, market performance, room occupancy, profitability and credit facilities in the hospitality sector of tourism in the tourist city of Livingstone in Zambia. The study used multiple regression models to find out the relationship between dependent and independent variables. The study found that there was positive impact of ICTs usage on sales, marketing performance, room occupancy, profitability and credit facilities. The study found negative relationship between internet and profitability of the firm due to higher costs of internet access. The study suggested that the firms should work together as a pool to reduce internet costs, such as, the use of trivago.co.zm; booking.co.zm; hotels.com; agoda.com; expedia.com; etc.
Tourism and Technology - New ways to create an engaging ExperiencePedro Tavares
Tourism is facing big challenges and consumer behavior is changing really fast.
This presentation shows some trends and ideas on how to create engaging experiences combining innovation with tourism
Smart tourism is an upcoming and novel exhortation applied to portray the growing reliance of tourism destinations globally. The tourism industry and its consumers (tourists) are emerging forms of information and communications technology (ICT) that permit for substantial quota of information in the form of data to be modified into value propositions. Nevertheless, it remains vague concept, which hampers its theoretical development. The efforts in this study are put together for defining smart tourism, and the research sheds light on present trends in smart tourism, and then laying out its business and technological establishment. This is pursued by a concise dialogue on the scenario and limitations of smart tourism. The research further draws attention to the immense call for investigation to enlighten smart tourism management and development in present scenario.
Guest lecture, delivered to masters students at Università Ca' Foscari Venezia, Italy, covering digital marketing, social media, mobile and e-learning technologies. Prepared and delivered April 2011.
This study aimed to investigate the impact of mobile phone, land phone and internet (ICTs) on sales, market performance, room occupancy, profitability and credit facilities in the hospitality sector of tourism in the tourist city of Livingstone in Zambia. The study used multiple regression models to find out the relationship between dependent and independent variables. The study found that there was positive impact of ICTs usage on sales, marketing performance, room occupancy, profitability and credit facilities. The study found negative relationship between internet and profitability of the firm due to higher costs of internet access. The study suggested that the firms should work together as a pool to reduce internet costs, such as, the use of trivago.co.zm; booking.co.zm; hotels.com; agoda.com; expedia.com; etc.
Tourism and Technology - New ways to create an engaging ExperiencePedro Tavares
Tourism is facing big challenges and consumer behavior is changing really fast.
This presentation shows some trends and ideas on how to create engaging experiences combining innovation with tourism
Resonance 2019-worlds-best-cities-reportDavid Mora
Informe de la consultora Resonance, con un ranking de las "mejores" ciudades del mundo según sus seis pilares. Barcelona aparece como quinta mejor ciudad y Madrid, como la undécima.
Learn about how URBAN-X Cohort 02 company Citiesense organizes the most accurate information about neighborhoods in cities – such as storefront vacancy, sales, foot traffic, and more – to better inform local market demand and neighborhood dynamics.
BIG DATA AND BIG CITIES THE PROMISES AND LIMITATIONSOF IMPR.docxtangyechloe
BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS
OF IMPROVED MEASURES OF URBAN LIFE
EDWARD L. GLAESER, SCOTT DUKE KOMINERS, MICHAEL LUCA and NIKHIL NAIK∗
New, “big data” sources allow measurement of city characteristics and outcome
variables at higher collection frequencies and more granular geographic scales than
ever before. However, big data will not solve large urban social science questions
on its own. Big urban data has the most value for the study of cities when it allows
measurement of the previously opaque, or when it can be coupled with exogenous shocks
to people or place. We describe a number of new urban data sources and illustrate how
they can be used to improve the study and function of cities. We first show how Google
Street View images can be used to predict income in New York City, suggesting that
similar imagery data can be used to map wealth and poverty in previously unmeasured
areas of the developing world. We then discuss how survey techniques can be improved to
better measure willingness to pay for urban amenities. Finally, we explain how Internet
data is being used to improve the quality of city services. (JEL R1, C8, C18)
I. INTRODUCTION
Historically, most research on urban areas
has relied on coarse aggregate statistics and
smaller-scale surveys. Over the past decade,
∗The authors would like to acknowledge helpful com-
ments from Andy Caplin, William Kominers, Jonathan Smith,
and Mitchell Weiss. E.L.G. acknowledges support from the
Taubman Center for State and Local Government; S.D.K.
acknowledges support from the National Science Foundation
(grants CCF-1216095 and SES-1459912), the Harvard Mil-
ton Fund, the Ng Fund of the Harvard Center of Mathematical
Sciences and Applications, and the Human Capital and Eco-
nomic Opportunity Working Group (HCEO) sponsored by
the Institute for New Economic Thinking (INET); and N.N.
acknowledges support from The MIT Media Lab consortia.
Glaeser: Department of Economics, Harvard University,
Cambridge, MA 02138; John F. Kennedy School of Gov-
ernment, Harvard University, Cambridge, MA 02138;
National Bureau of Economic Research, Cambridge, MA
02138. Phone 617-496-2150, Fax 617-495-3817, E-mail
[email protected]
Kominers: Department of Economics, Harvard University,
Cambridge, MA 02138; Center of Mathematical Sciences
and Applications, Harvard University, Cambridge, MA
02138; Center for Research on Computation and Soci-
ety, Harvard University, Cambridge, MA 02138; Pro-
gram for Evolutionary Dynamics, Harvard University,
Cambridge, MA 02138; Entrepreneurial Management,
Harvard Business School, Boston, MA 02163; Soci-
ety of Fellows, Harvard University, Cambridge, MA
02138. Phone 617-495-8407, Fax 617-495-3817, E-mail
[email protected]
Luca: Negotiation, Organizations & Markets, Harvard Busi-
ness School, Boston, MA 02163. Phone 845-549-0372,
Fax 617-495-3817, E-mail [email protected]
Naik: Media Lab, Massachusetts Institute of Technology,
Cambridge, MA 02139. Phone 617-758-9.
Emerging Technology, Shiny Objects & The Future of Media - iSummit - Fred SteubeFred Steube
The rapid pace of digital innovation has media companies scrambling to figure out which new emerging technology will be a hit with consumers and how to reach these consumers on these many new channels. Traditional media like print, TV, radio, and outdoor media will need to take advantage of wearables, beacons, digital wallets, augmented reality, etc and will have to respond to disruptive technology to remain competitive in an increasingly dynamic business landscape.
How relevant is the age of a city in determining its interest in, and ability to use, 'big data'? This briefing explores how both old and new cities have distinct advantages and disadvantages in their ability to use big data effectively, the lessons they can learn from each other, and their common challenges.
Mobile Payments: Growth - Country Comparison - Usage; Whitepaper 2017Statista
This whitepaper provides insights on growth, compares the main markets, provides consumer views and highlights which mobile payment apps are popular.
More information: statista.com
The presentation started with the work frame of Dalberg Data Insights which entailed the acquisition of information from MTN to help provide solutions to energy-related development problems. Dalberg provides mobile money dashboards to guide decision making for instance digitizing value chains. The dashboard showed that 2.73K for mobile money payments for made are for solar. From the information, they realized that 60% of Uganda’s population own phones. 43.6% have data-enabled phones and the percentage of those with basic phones keeps lowering. He mentioned that having such information can help to inform for instance the marketing channels to be used by a solar company.
Similar to BBVA - Territorial Analysis based on Financial Activity Data (20)
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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.
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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.
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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:
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Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
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2. Who are we? What do we do?
What data do we work with?
Research partnerships results
Visualizations
Projects
3. The core Bank of current BBVA Group was
founded in Bilbao in 1857
4. BBVA Data & Analytics, was established in
2014 as a new Data Science Center
…its origin was a
research group at
BBVA Innovation
Center, 2011
Our mission is to extract the value
enclosed in BBVA’s data:
-data engines development
-data-based products and
services
-data-based ad-hoc
consultancy projects
5. Who are we? What do we do?
What data do we work with?
Research partnerships results
Visualizations
Projects
6. ► Card payments data generate a digital footprint
that can be read to describe socio-economic activity
7. ► Commerce and territory: a measure of prosperity
Sources: World
Bank, INE,
INEGI
Consumption spending
makes a major fraction of
GDP
Commerce, hotel and catering
services have great influence on
employment
Tourism influence on GDP is
also a key factor
España 58% 29% 10,9%
México 66% 27% 8,7%
8. Commercial activity registered by BBVA electronic payment systems in Spain [2014]
524 million transacctions*
(*BBVA cards+Non BBVA)
24 billion €
(*BBVA cards+Non BBVA)
48 million different cardholders
[(Spanish: BBVA+Non BBVA) + (foreigners: Non BBVA)]
More than 1 million comercial premises,
(BBVA+Non BBVA PoS)
9. ► Research steps and objectives
From data analysis… … to innovation.
{X, Y, t, €} Activity and
behavioral patterns
Insights, visualizations
and applications
Analyze people’s
interests and mobility
Measure permeability
and attractiveness of
cities
Demonstrate
hyperscalability factors
Design and implement
interactive tools
10. -How much? spending (€), number of transactions, average ticket
-Where? (X,Y,C) where C=Commercial type assigned to the PoS
-When? Time aggregations, frecuency, payments patterns
-Who? Anonymous consumer profile:
·Origin (residence zip code for BBVA cardholders, country for non BBVA cardholders)
·Gender, age (BBVA cardholders)
·Inferred characteristics: purchasing power, behavioral segmentatión, preferences
and interests
DESTINATIONORIGIN
Multidimensional
data
►Descriptive capacity of this
kind of data
11. -BBVA cards used on any kind of PoS:
·provides visión about the whole
transactional serie
Non BBVA cards on BBVA PoS:
·Non continuous activity track, low
frequency informationhard to track
itineraries
►Data sources and sample representativity
B=TPVs BBVA
A=BBVA cardholders
Points of Sale
Cardholders
Y%
100%
X% 100%
Vision on P% card
transactions:
P=(AUB)=X·1+1·Y-(X·Y)
12. City/Region Neighborhood commercial area
►We do apply privacy filters to generate statistics aggregating transactions
Descriptive
information:
Commercial
type
breakdown
Cardholder
features
Time
resolution:
year
month
week
day
hour
13. Who are we? What do we do?
What data do we work with?
Research partnerships results
Visualizations
Projects
15. 15
1. Mining urban performance: Scale-independent classification of cities based on individual
economic transactions. Sobolevsky, S., Sitko, I., Grauwin, S., Combes, R. T. D., Hawelka, B., Murillo
Arias, J., & Ratti, C. (2014). arXiv preprint arXiv:1405.4301. Fifth ASE International Conference on Data
Science in Stanford, CA, May, 2014
2. Money on the move: Big data of bank card transactions as the new proxy for human mobility
patterns and regional delineation. the case of residents and foreign visitors in spain.Sobolevsky, S.,
Sitko, I., Tachet des Combes, R., Hawelka, B., Murillo Arias, J., & Ratti, C. (2014, June). In Big Data
(BigData Congress), 2014 IEEE International Congress on (pp. 136-143). IEEE.
3. Cities through the Prism of People's Spending Behavior. Sobolevsky, S., Sitko, I., Combes, R. T. D.,
Hawelka, B., Arias, J. M., & Ratti, C. (2015)..arXiv preprint arXiv:1505.03854. Submitted to PLOS ONE
4. Scaling of city attractiveness for foreign visitors through big data of human economical and
social media activity. Sobolevsky, S., Bojic, I., Belyi, A., Sitko, I., Hawelka, B., Arias, J. M., & Ratti, C.
(2015).. arXiv preprint arXiv:1504.06003. IEEE Big Data Congress’2015 in NYC
5. Predicting Regional Economic Indices Using Big Data Of Individual Bank Card Transactions.
Sobolevsky, S., Massaro, E., Bojic, I., Arias, J. M., & Ratti, C. (2015). arXiv preprint arXiv:1506.00036.
Sixth ASE International Conference on Data Science in Stanford, CA, August, 2015 (best paper award)
6. Influence of sociodemographics on human mobility. Maxime Lenormand, Thomas Louail, Oliva G.
Cantu Ros, Miguel Picornell, Ricardo Herranz, Juan Murillo Arias, Marc Barthelemy, Maxi San Miguel, and
José J. Ramasco
Scientific papers
16. ►Beyond official administrative divisions, what are the functional inner boundaries
of a country? What are major cities’ areas of influence?
19. City
attractiveness is
defined as the
absolute number
of photographs,
tweets or
economical
transactions
made in the city
by foreign
visitors.
City attractivenes
follows a
superlinear
correlation with
cities’ size in
terms of
population.
20. Figure 3 visualizes
residuals for the LUZs
ordering the cities from
the most overperforming
to the most
underperforming ones
according to the bank
card transactions data. It
can be noticed that
although residuals from
different datasets are
different, the patterns are
generally consistent -
cities strongly
over/under-performing
according to one dataset
usually do the same
according to the others.
21. Commercial index project
Comparative quantitative analysis of
microeconomic climate of the cities:
measuring location’s success
and opportunities
Ability to compete regions, cities, locations
Investment attractiveness
New business opportunities
Learn from the leaders – how to improve
Enrich census and official statistics
22. Objectives
From micro to macro... and back to micro
Build a model that predicts official statistics
at province level
Apply that model at higher resolution
levels: geographical units below province,
temporal variation below year/month
Custom business predictions: opportunity
areas, risks predictions
23. How may we define quality of life?
Economic parameters:
GDP
Housing prices
Unemployment
Social parameters:
Crime
Education
Life expectancy
Subjective well-being:
happiness
self esteem
self realization
human interactions (f&f)
(lack of qualitative
dense and reliable data)
32. GDP – visualization of the model
fit
Sobolevsky, S., Massaro, E., Bojic, I., Arias, J. M., & Ratti, C. (2015). Predicting Regional Economic Indices Using Big Data Of Individual Bank Card
Transactions. arXiv preprint arXiv:1506.00036. Sixth ASE International Conference on Data Science in Stanford, CA, August, 2015 (best paper award)
Offcial Statistics Commercial Indexes Model
33. Who are we? What do we do?
What data do we work with?
Research partnerships results
Visualizations
Projects
47. Spending distribution
Playa del
Carmen
(16,80%)
Isla Mujeres
(0,33%)
Cozumel
(2,64%)
Cancún
(80,23%)
Riviera Maya
registró el 3,73% del
gasto total realizado
en México en el año
2014*
* el análisis está referido únicamente al gasto efectuado por clientes Bancomer
51. Origin of national visitors to Cancún
according to their spending
100%0% 1% 5% 10% 20%
Turismo nacional Sin incluir México y DF
Normalizado según la
población de cada estado*
0 50 300200100
*Base 100 si el peso del gasto realizado por los residentes de un estado coincide con el peso demográfico de dicho estado en el conjunto de la nación
Cancún:
• Distrito Federal (24,06%)
• México (23,49%)
• Jalisco (6,57%)
• Nuevo León (4,68%)
• Puebla (3,48%)
• Resto estados (37,72%)
Cancún:
• Jalisco (12,52%)
• Nuevo León (8,93%)
• Puebla (6,64%)
• Veracruz (6,38%)
• Tabasco (5,53%)
• Resto estados (60%)
Cancún:
• Distrito Federal (374)
• México (213)
• Campeche (204)
• Tabasco (178)
• Quintana Roo (175)
BBVA has a strong link to cities: day by day, second by second, we deal with a time ordered flow of geopositioned data: not only commercial transacions, but money transfers, communications, etc. and we can turn it into useful information that constitute the foundation for better internal and external decision taking processes
Pero sin duda el más complejo de todos estos sistemas es la dinámica socioeconómica, una capa intangible que abarca las interacciones entre las administraciones, las empresas y los ciudadanos en su doble faceta:
Como usuarios de servicios públicos (educación, cultura, sanidad, seguridad, gobierno)
Como consumidores de productos y servicios empresariales (comercio, s. financiero, asesoría, alojamiento y restauración, etc.)
La estructura de los datos responde a distintos niveles de agregación espacial y temporal... (leer diapo)
La estructura de los datos responde a distintos niveles de agregación espacial y temporal... se necesita un tamaño minimo para que –una vez filtrados los datos por criterios de privacidad- las estadísticas sean elocuentes.
Objectives of the project are manifold. We first start from evaluating the approach by training the model to predict existing official economical statistics – this will be the goal of the presented work. Further steps are: adapt the model for various spatial scales, capture temporal variation of regional performance, predict other relevant characteristics of urban life and finally focus on specific business use-cases for making custom business predictions
In this initial study we utilize 6 most common quantities from a variety of parameters provided by INE to characterize regional performance on the province scale.
From the other hand our data provides diverse multi-dim insights on human activity in the areas
From the other hand our data provides diverse multi-dim insights on human activity in the areas
Here are the characteristics we’re looking at which would all together build up our feature space for learning the models
The model will have several phases
Normalization – brining all the quantities on the same temporal scale by fitting the distribution and normalizing towards it.
Dimensionality reduction
Training generalized linear model
Testing performance
For the initial evaluation we picked up a fairly simple model – partially because this is always a first reasonable step in machine-learning, partially because the small size of data sample we deal with prevent from efficiently utilizing more sophisticated learning techniques, such as decision trees or neural networks.
First logistic regression predicts normalized versions of the statistical quantities (between 0 – worst and 1 - best) and then applying an inverse distribution we also learn from the training set we predict the actual values on the original scale
15 PCAs cover 95% of the entire information, but learning curve on the right show that optimal performance on the validation samples is typically achieved while considering just 6.
Here is how they can be characterized by the impact
Decent performance: 50-60% on the validation sets vs 60-70% on the training. Exception: crime rate where performance on the non-normalized scale is strongly affected by several outliers
And here, in this field, is where BBVA can make an important contribution.
We have a strong link to cities: day by day, second by second, we deal with a time ordered flow of geopositioned data, and we can turn it into useful information that can constitute the foundation for better decision taking processes