Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
Rapid digitization has resulted in the production of large volumes of unstructured data. This trend is expected to provide significant opportunities for graph database market in the upcoming years
This session is a continuation of “Apply MLOps at Scale” at Data+AI Summit Europe 2020 and “Automated Production Ready ML at Scale” at Spark AI Summit at Europe 2019. In this session you will learn how H&M is continuing to evolve and develop their AI platform in order to democratize and accelerate AI usage across the full H&M group, including speed to production, data abstraction, feature store, pipeline orchestration, etc.
Our existing reference architecture has been adapted by multiple product teams managing 100’s of models across the entire H&M value chain and enables data scientists to develop model in a highly interactive environment, enabling engineers to manage large scale model training and model serving pipeline with full traceability. The current evolution aims to both reduce the time to introduce new features to the market as well as the learning feedback loop by democratizing AI in the organisation and persistent focus on sound MLOps principles.
Big Data LDN 2018: SHAPING AN AI-DRIVEN FUTURE WITH AUGMENTED INTELLIGENCE FO...Matt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 15:50 - 16:20
Speaker: Patrice Neff
Organisation: Squirro
About: Machine learning and AI need huge amounts of data to train good algorithms. Even in today's Big Data landscape companies still struggle to get access to enough data to train systems. Squirro solves this problems in two ways: easy data access and Pragmatic AI.
Squirro's pragmatic AI approach allows companies to very quickly gain value from their data, without having to spend weeks on training machine learning models.
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATIONMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:50 - 14:20
Speaker: Becky Smith
Organisation: Denodo
About: How many users inside and outside of your organization access your organization’s data? Dozens? Hundreds is probably more like it, each with their own structure and content requirements as well as different access rights. As a result, many organizations have witnessed the formation of “data delivery mills,” in various shapes and sizes. How does one create order and reliability in this world of chaotic data streams? Quite easily, if it’s done with data virtualization.
According to Gartner, "through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration.” Data virtualization enables organizations to gain data insights from multiple, distributed data sources without the time-consuming processes of data extraction and loading. This allows for faster insights and fact-based decisions, which help business realize value sooner.
Join us to find out more about:
• What data virtualization actually means and how it differs from traditional data integration approaches.
• How you can connect and combine all your data in real-time, without compromising on scalability, security or governance.
• The benefits of data virtualization and its most important use cases.
Creating an Omnichannel Banking Experience with Machine Learning on Azure Dat...Databricks
Ceska sporitelna is one of the largest banks in Central Europe and one it’s main goals is to improve the customer experience by weaving together the digital and traditional banking approach. The talk will focus on the story of how in order to reach this goal Ceska Sporitelna created a new team focused on building use cases on top of a combined digital and offline customer engagement 360 powered by a Spark and Databricks-centric agile advanced analytics platform in the Azure cloud combined with a on-prem data lake. This talk will cover:
The customer engagement 360 vision powered by machine learning and the cloud
Deep dive into the use case of optimizing and personalizing programmatic ad buying on the individual user and ad placement level thanks to Spark MLLib and NLP on top of hundreds of millions of ad interaction data
Deep dive into the use case of supporting the seamless transition of the customer journey from digital to traditional offline channels
The approach to building the agile analytics platform and experience of adopting the cloud in a EU-regulated financial institution
Big Data LDN 2018: THE NEXT WAVE: DATA, AI AND ANALYTICS IN 2019 AND BEYONDMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:10 - 13:40
Speaker: Matt Aslett
Organisation: 451 Research
About: As 2018 draws to a close, Matt Aslett, Research VP, 451 Research looks ahead to 2019 and the key trends the research company’s Data, AI and Analytics team is anticipating for the year ahead, including the continued rise of DataOps; the increased importance of data science operationalisation; mainstream adoption of AI and machine learning; data platforms evolution; and the confluence of distributed database and blockchain technology in supporting the move towards planetary-scale data processing and analytics.
Big Data Analytic with Hadoop: Customer StoriesYellowfin
Why watch?
Looking to analyze your growing data assets to unlock real business benefits today? But, are you sick of all the Big Data hype and whoopla?
Watch this on-demand Webinar from Actian and Yellowfin – Big Data Analytics with Hadoop – to discover how we’re making Big Data Analytics fast and easy:
Learn how a telecommunications provider has already transformed its business using Big Data Analytics with Hadoop.
Hold on as we go from data in Hadoop to predictive analytics in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
What will you learn?
Discover how Actian’s market-leading Big Data Analytics technologies, combined with Yellowfin’s consumer-oriented platform for reporting and analytics, makes generating value from Big Data Analytics faster and easier than you thought possible.
Join us as we demonstrate how to:
• Connect to, prepare and optimize Big Data in Hadoop for reporting and analytics.
• Perform predictive analytics on streaming Big Data: Learn how to empower all your analytics stakeholders to move from historical reports to predictive analytics and gain a sustainable competitive advantage.
• Communicate insights attained from Big Data: Optimize the value of your Big Data insights by learning how to effectively communicate analytical information to defined user groups and types.
This Webinar is ideal if…
• You want to act on more data and data types in shorter timeframes
• You want to understand the steps involved in achieving Big Data success – both front and back end
• You want to see how market leaders are leveraging Big Data to become data-driven organizations today
Looking to analyze and exploit Big Data assets stored in Hadoop? Then this Webinar is a must.
Up Your Analytics Game with Pentaho and Vertica Pentaho
Big Data is a game-changer.
In the face of exploding volumes and varieties of data, traditional data management and ETL systems just aren’t cutting it anymore. A new way of sifting through vast volumes of data to find the most relevant info, combining this data with other data sources to extract faster insights is desperately needed. Enter HP|Vertica and Pentaho with a proven solution for lightning fast queries and blended data and analytics capabilities for your business users.
Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
Rapid digitization has resulted in the production of large volumes of unstructured data. This trend is expected to provide significant opportunities for graph database market in the upcoming years
This session is a continuation of “Apply MLOps at Scale” at Data+AI Summit Europe 2020 and “Automated Production Ready ML at Scale” at Spark AI Summit at Europe 2019. In this session you will learn how H&M is continuing to evolve and develop their AI platform in order to democratize and accelerate AI usage across the full H&M group, including speed to production, data abstraction, feature store, pipeline orchestration, etc.
Our existing reference architecture has been adapted by multiple product teams managing 100’s of models across the entire H&M value chain and enables data scientists to develop model in a highly interactive environment, enabling engineers to manage large scale model training and model serving pipeline with full traceability. The current evolution aims to both reduce the time to introduce new features to the market as well as the learning feedback loop by democratizing AI in the organisation and persistent focus on sound MLOps principles.
Big Data LDN 2018: SHAPING AN AI-DRIVEN FUTURE WITH AUGMENTED INTELLIGENCE FO...Matt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 15:50 - 16:20
Speaker: Patrice Neff
Organisation: Squirro
About: Machine learning and AI need huge amounts of data to train good algorithms. Even in today's Big Data landscape companies still struggle to get access to enough data to train systems. Squirro solves this problems in two ways: easy data access and Pragmatic AI.
Squirro's pragmatic AI approach allows companies to very quickly gain value from their data, without having to spend weeks on training machine learning models.
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATIONMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:50 - 14:20
Speaker: Becky Smith
Organisation: Denodo
About: How many users inside and outside of your organization access your organization’s data? Dozens? Hundreds is probably more like it, each with their own structure and content requirements as well as different access rights. As a result, many organizations have witnessed the formation of “data delivery mills,” in various shapes and sizes. How does one create order and reliability in this world of chaotic data streams? Quite easily, if it’s done with data virtualization.
According to Gartner, "through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration.” Data virtualization enables organizations to gain data insights from multiple, distributed data sources without the time-consuming processes of data extraction and loading. This allows for faster insights and fact-based decisions, which help business realize value sooner.
Join us to find out more about:
• What data virtualization actually means and how it differs from traditional data integration approaches.
• How you can connect and combine all your data in real-time, without compromising on scalability, security or governance.
• The benefits of data virtualization and its most important use cases.
Creating an Omnichannel Banking Experience with Machine Learning on Azure Dat...Databricks
Ceska sporitelna is one of the largest banks in Central Europe and one it’s main goals is to improve the customer experience by weaving together the digital and traditional banking approach. The talk will focus on the story of how in order to reach this goal Ceska Sporitelna created a new team focused on building use cases on top of a combined digital and offline customer engagement 360 powered by a Spark and Databricks-centric agile advanced analytics platform in the Azure cloud combined with a on-prem data lake. This talk will cover:
The customer engagement 360 vision powered by machine learning and the cloud
Deep dive into the use case of optimizing and personalizing programmatic ad buying on the individual user and ad placement level thanks to Spark MLLib and NLP on top of hundreds of millions of ad interaction data
Deep dive into the use case of supporting the seamless transition of the customer journey from digital to traditional offline channels
The approach to building the agile analytics platform and experience of adopting the cloud in a EU-regulated financial institution
Big Data LDN 2018: THE NEXT WAVE: DATA, AI AND ANALYTICS IN 2019 AND BEYONDMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:10 - 13:40
Speaker: Matt Aslett
Organisation: 451 Research
About: As 2018 draws to a close, Matt Aslett, Research VP, 451 Research looks ahead to 2019 and the key trends the research company’s Data, AI and Analytics team is anticipating for the year ahead, including the continued rise of DataOps; the increased importance of data science operationalisation; mainstream adoption of AI and machine learning; data platforms evolution; and the confluence of distributed database and blockchain technology in supporting the move towards planetary-scale data processing and analytics.
Big Data Analytic with Hadoop: Customer StoriesYellowfin
Why watch?
Looking to analyze your growing data assets to unlock real business benefits today? But, are you sick of all the Big Data hype and whoopla?
Watch this on-demand Webinar from Actian and Yellowfin – Big Data Analytics with Hadoop – to discover how we’re making Big Data Analytics fast and easy:
Learn how a telecommunications provider has already transformed its business using Big Data Analytics with Hadoop.
Hold on as we go from data in Hadoop to predictive analytics in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
What will you learn?
Discover how Actian’s market-leading Big Data Analytics technologies, combined with Yellowfin’s consumer-oriented platform for reporting and analytics, makes generating value from Big Data Analytics faster and easier than you thought possible.
Join us as we demonstrate how to:
• Connect to, prepare and optimize Big Data in Hadoop for reporting and analytics.
• Perform predictive analytics on streaming Big Data: Learn how to empower all your analytics stakeholders to move from historical reports to predictive analytics and gain a sustainable competitive advantage.
• Communicate insights attained from Big Data: Optimize the value of your Big Data insights by learning how to effectively communicate analytical information to defined user groups and types.
This Webinar is ideal if…
• You want to act on more data and data types in shorter timeframes
• You want to understand the steps involved in achieving Big Data success – both front and back end
• You want to see how market leaders are leveraging Big Data to become data-driven organizations today
Looking to analyze and exploit Big Data assets stored in Hadoop? Then this Webinar is a must.
Up Your Analytics Game with Pentaho and Vertica Pentaho
Big Data is a game-changer.
In the face of exploding volumes and varieties of data, traditional data management and ETL systems just aren’t cutting it anymore. A new way of sifting through vast volumes of data to find the most relevant info, combining this data with other data sources to extract faster insights is desperately needed. Enter HP|Vertica and Pentaho with a proven solution for lightning fast queries and blended data and analytics capabilities for your business users.
f your company is caught up wondering which mobile apps to build or which devices to support, chances are you’re asking the wrong questions. Instead, organizations need to understand first how user expectation is being rewired in a mobile world - one in which “mobile moments” are the new battleground for customer and employee engagement.
TechConnectr's Big Data Connection. Digital Marketing KPIs, Targeting, Analy...Bob Samuels
This presentation was given at the Deep Dive Conference in November. 2013.
Big Data Applications... example, digital marketing, and targeting and optimization...
Feedback, and additional perspectives, is appreciated.
Thank you,
Bobby Samuels
TechConnectr.com
Bridging the Personalization Gap - Adnan SauafSITA
There is a lot of talk about personalizing the message and offers to your customers but sadly the research shows that companies are not even identifying the customer correctly instead using multiple marketing tools to bombard the customer with competing offers across channels.
Customer data platforms (CDPs) are now ubiquitous and have created a lot of buzz. But many marketers are still unsure exactly how they work and how they enhance advertising strategies. The answer: nothing is more important than first-party customer data. Learn how to successfully optimize your current marketing efforts by utilizing your first-party customer data and enhance your value proposition messaging to grow sales.
Delivering on Personalization with the Power of APIsAkana
• Why is personalization important for capturing and delighting customers?
• What are the main drivers of personalization, with examples?
• What is an API?
• How are companies using APIs and personalization to rethink the customer experience?
• How can companies innovate to deliver a more personalized experience with APIs?
Helping brands to foster deeper customer relationships mParticle
A brief introduction to the mParticle Customer Data Platform. In 5 mins learn how mParticle's API-powered consumer data platform is used by customer-centric organizations to fuel amazing Customer Experiences and improve Customer Lifetime Value.
Alex Kesaris
akesaris@mparticle.com
+447400999957
Increase online growth: In 4 steps optimal data orchestration OrangeValley
The global COVID-19 outbreach has led to an enormous increase in online traffic. This is already clearly visible in the Healthcare, Food, Finance and Media industries. This growth in online traffic directly leads to an increase in (customer) data, the question remains: How can you optimally orchestrate this sea of data to facilitate online growth?
Learn how direct response marketers are adapting their strategies, growing revenue and beating their competition as their audience moves to mobile. Attendees will hear case studies highlighting best practices for lead generation, click-to-call and mobile commerce, and will learn successful strategies on how to apply direct response techniques in the mobile world.
This presentation was given by Ted McNulty, Millennial Media's Senior Director of Performance Sales, at NEDMA's 2014 Marketing Technology Summit.
Similar to Building an accurate understanding of consumers based on real-world signals (20)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. About Near
2
Enabling you to understand consumers better with real-world data, and engage with them.
SaaS products for
real-world data enrichment
& data-driven marketing
World's largest source
of intelligence on
People and Places
Bring massive data into an
AI-powered unified platform to
understand consumer behavior
1.6 Billion
Users
44
Countries
GDPR compliant | No PII data |
Consent driven incoming data
Scale/Data Privacy-led design
70 Million+
Places
Processing petabyte of data on monthly basis
3. Corporate Overview
3
Marquee Investors
San Francisco
New York
Bangalore
Tokyo
Singapore
Sydney
Office HQ
2012
Established Year
USD $134mn to date
Capital
Trusted by
London
Product / Tech / Core Biz
France
4. 4
Agenda
Near - Improving Return on Investment
Probabilistic Mapping of People and Places
Reaching Out to Consumers Based on Real-world Signals
Classifying Staypoints and Waypoints
Curating and Analyzing Audience Profiles
Store Footfall Attribution
5. 5
Near - Improving Return on Investment
Staypoints /
Waypoints
Identify if the consumer is at a brick & Mortar
store or walking/commuting/flying while
emitting location pings from the device
Audience profile
Create bespoke audiences using ML/AI
algorithms, which learn from both online
and offline signals
Ping-to-POI
assignment
Accurate assignment of people
visits to brick & mortar stores to
understand offline behavior
Store footfalls
attribution
Measure campaign efficacy based on
store footfalls using large scale
geofenced polygon database
Improved
ROI
6. 6
Near - Improving Return on Investment
Staypoints Waypoints
● Footfalls
● Offline user behavior
● User profiles
● Insights
○ Dwell time
○ Distance travelled
○ Brand affinity
○ Brand propensity
● Historical visit behavior
● Home location
● Work location
● OOH advertising
● Commute patterns
● Commute time
● Navigation search
7. 7
How Near Extracts Staypoints?
Location Pings from a consumer Broken Sessions Staypoints
Staypoints
Place Matrix
Retrieve endpoint of a session
(staypoints) by overlaying places
Time interval
between
pings
Distance
between
pings
Speed
Dwell time
at a place
Post applying below conditions, algorithm extracts multiple sessions for the user
8. 8
How do we probabilistically map people to places?
Place Matrix
Place Metadata
Historical Visit
App History
Transaction
Wifi Signal
Bayesian Inference
Model Framework
Staypoints
Ping-to-POI
Mapping
Example of Distance-based Mapping
Probabilistic Mapping of people to places
using online and offline consumer behavior
9. 9
How Near Segments Audience Profiles?
We have successfully surpassed the Gender/Age Group on-targeting
benchmarks in USA and extending to other markets such as
ANZ, SEA, MEA and APAC
Near ML / AI Platform
Ping-to-POI
Mapping
Place DB
Historical Visits
Device Apps /
Usage
Online Behavior
Device / Signature
Offline / Online
Data store
Entity Resolution
Supervised Learning
Ground Truth
Labeling
Semi-supervised
Learning
Natural Language
Understanding
Reinforcement
Learning
Transactions
People Properties Place Properties
Cloud Engineering MLOps Feature Engineering
Jobs Orchestration
Container
Orchestration
SQL/NoSQL
Databases
Gender
Age Group
Profiles
Interests
Brand Affinity
Affluence
Ethnicity
Home Location
Work Location
Household
Place Boundary
Brand
Category
Store Hours
Gender Distribution
Age Group Dist.
Profile Dist.
Footfalls
Dwell Time
Distance Travelled
Frequent Visitors
Attribute
Store
Consumer attribute
Store
Places attribute
Store
Nielsen Digital Ad Ratings
10. 10
How Near does Store Footfall Attribution?
Digital Campaign
Footfalls
Exposed Group
(from Digital
Campaign)
Control Group
(ActAlike)
Footfalls are measured for pre,
during and post-campaigns
1. Footfalls are measured with
the most accurate
information from location
pings and places DB
2. Near Places DB is one of
the largest source of
Building Place Boundaries
and we are adding more
every day
3. Location Pings are curated
for staypoints and removing
any anomalies in the data
4. We use distributed
technologies to couple the
two sources over large scale
(several TBs every day)
5. Time series model
estimates / offsets any
missing data on a given day
Attribution
Footfalls during
COVID-19
Digital Campaign and Offline Attribution
11. 11
Use Case: Marketers / Advertisers can now target super-refined segments
With Allspark, you can analyze and reach out
to consumers based on
Gender &
Age Group
Profiles
Interests
Events Weather
Income /
Affluence
Home
Location
Brands
Affinity
Stores
Proximity
Curate Activate Measure
12. Allspark is SaaS Platform for
curating and activating
audiences through your choice
of a DSP and measure
campaign effectiveness
Simplified marketing
powered by the world’s
first AI audience assistant
Allspark
Carbon is an data enrichment
platform where we augment the
customer 360 view by using ML/AI
approaches for matching user
attributes both in offline and
online world
The best software
platform for data
enrichment
CARBONTM
Curate, Activate, MeasureData Enrichment
13. 13
Get Campaign Insights
Daily Offline
Attribution Report
Delivering Performance
Deliver Ads
Programmatically
Activate Campaigns using
Custom Creatives
Reaching Audience
Estimate
Campaign Reach
Curate Bespoke
Audiences
Media Planning
Pre-Sales Market
Research Reports
Export Audiences
To DSP
Advanced
End-of-campaign Report
Activation MeasurementAudience Curation
Allspark Supports the entire Marketing Campaign Lifecycle based on Real-world Behavior
16. THANK YOU
Connect with us at near.co
From inception, the Near Platform has followed a privacy-led design. The Platform never stores or deals with PII (Personally Identifiable Information)
and all incoming data streams are consensual. We are GDPR compliant, and the platform has built-in processes to forget and purge user data on
requests. Read the complete privacy policy at near.co/privacy