This document discusses an approach for creating a virtual assistant ecosystem using analytics and machine learning to optimize workflows. It describes using intent recognition and natural language processing to enable dynamic access to insights from device data. A demo is then presented of a chatbot interface that can understand requests and fulfill them using predictive analytics services.
IBM ConnectED SPOT104: Lightning-Fast Development of Native Mobile Apps for I...darwinodb
This is the presentation that John Tripp & Phil Riand made at IBMConnectED introducing Darwino, a new full-stack enterprise development platform for social and mobile cloud enabled applications that work natively on mobile devices - even offline.
IBM ConnectED SPOT104: Lightning-Fast Development of Native Mobile Apps for I...darwinodb
This is the presentation that John Tripp & Phil Riand made at IBMConnectED introducing Darwino, a new full-stack enterprise development platform for social and mobile cloud enabled applications that work natively on mobile devices - even offline.
Progressive f# tutorials nyc don syme on keynote f# in the open source worldSkills Matter
F# is a powerful open-source language which Microsoft, other companies and the F# community all contribute to. In this talk, Don will discuss how the “F# space” has recently opened up significantly in interesting ways. F# now includes contributions that range from Cloud IDE platforms, Cloud Compute frameworks, Data interoperability components, Cross-platform execution, Try F#, MonoDevelop, and even Emacs editor integration with surprising tooling support, as well as the Visual F# tools from Microsoft and the broader NuGet package ecosystem. Don will also talk about some of the latest contributions from Microsoft Research, including new type provider components for F#, and describe how his team work with the Visual F# team and other teams around Microsoft. There will also be demos of some fun new stuff that’s been going on with F# at MSR and the community.
Snap4City November 2019 Course: Smart City IOT platform installation, deploy,...Paolo Nesi
• Snap4City Architecture
• Snap4City: Smart City IOT as a Service
• Snap4City Living Lab For Collaborative Work
• Smart City Development Life Cycle
• Analysis and Design for Innovation (Co-Creation and Co-Working)
• Development Tools
• How to Add Functions that are not present in the Platform
• Snap4City vs Fi-Ware
• Snap4City vs State of the Art Solutions
• Snap4City Services: Consulting and Developing
• Snap4City vs Snap4Industry 4.0
• Installing Snap4City
• The view of the Administrator
• Monitoring Resource Consumption and Traffic
• Managing and Monitoring Data Traffic in the BackOffice
• Auditing Activities
• Managing Back Office processes via Containers
• Acknowledgement
Extend your Oracle Forms Application with Chatbots. A Chat Interface to your back-office system, for faster and efficient transactions.
Transforming the User Experience, without having to touch a single line of code!
For more information on AuraPlayer and on Oracle Forms:
Website: http://www.auraplayer.com/
Blog: http://oracleformsinfo.com/
Twitter: @AuraPlayer @MiaUrman
Distributed and/or parallel code is normally based on elaborate platforms. The main problems with such platforms are (1) constraints placed on operation of the code and (2) overhead imposed by the platform that arbitrates among multiple instances within the running code. This paper argues in favor of platform-less distribution of code. The base unit is referred to as 3-way script, where the three ways are (1) calling a method/function of an instantiated class, (2) executing the code from the command line, and (3) calling a method/function using HTTP requests to a remote web API. The key merit of the proposal is that all the three uses are possible on the same code, which by developer only one -- this code is referred to as a 3-way script. This paper discusses examples of the code written in PHP, while the same design is possible in several other popular programming languages.
How to Hybrid : Effective Tactics in HTML5-Native App DevelopmentDroidConTLV
Gartner has predicted that by 2016, “more Than 50 Percent of Mobile Apps Deployed Will be Hybrid.” Knowing how and when to utilize HTML5 technology in your application will help you prepare for that future. This lecture will cover several techniques and real life examples on how to utilize hybrid development in your applications. The tools and tactics for how to connect (or bridge) your “native” Java code implementations with HTML5 will be presented with code samples. The lecture will also cover the right and wrong ways to implement HTML5 in your application, and when to “stick to native.”
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
More Related Content
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Progressive f# tutorials nyc don syme on keynote f# in the open source worldSkills Matter
F# is a powerful open-source language which Microsoft, other companies and the F# community all contribute to. In this talk, Don will discuss how the “F# space” has recently opened up significantly in interesting ways. F# now includes contributions that range from Cloud IDE platforms, Cloud Compute frameworks, Data interoperability components, Cross-platform execution, Try F#, MonoDevelop, and even Emacs editor integration with surprising tooling support, as well as the Visual F# tools from Microsoft and the broader NuGet package ecosystem. Don will also talk about some of the latest contributions from Microsoft Research, including new type provider components for F#, and describe how his team work with the Visual F# team and other teams around Microsoft. There will also be demos of some fun new stuff that’s been going on with F# at MSR and the community.
Snap4City November 2019 Course: Smart City IOT platform installation, deploy,...Paolo Nesi
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• Installing Snap4City
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Extend your Oracle Forms Application with Chatbots. A Chat Interface to your back-office system, for faster and efficient transactions.
Transforming the User Experience, without having to touch a single line of code!
For more information on AuraPlayer and on Oracle Forms:
Website: http://www.auraplayer.com/
Blog: http://oracleformsinfo.com/
Twitter: @AuraPlayer @MiaUrman
Distributed and/or parallel code is normally based on elaborate platforms. The main problems with such platforms are (1) constraints placed on operation of the code and (2) overhead imposed by the platform that arbitrates among multiple instances within the running code. This paper argues in favor of platform-less distribution of code. The base unit is referred to as 3-way script, where the three ways are (1) calling a method/function of an instantiated class, (2) executing the code from the command line, and (3) calling a method/function using HTTP requests to a remote web API. The key merit of the proposal is that all the three uses are possible on the same code, which by developer only one -- this code is referred to as a 3-way script. This paper discusses examples of the code written in PHP, while the same design is possible in several other popular programming languages.
How to Hybrid : Effective Tactics in HTML5-Native App DevelopmentDroidConTLV
Gartner has predicted that by 2016, “more Than 50 Percent of Mobile Apps Deployed Will be Hybrid.” Knowing how and when to utilize HTML5 technology in your application will help you prepare for that future. This lecture will cover several techniques and real life examples on how to utilize hybrid development in your applications. The tools and tactics for how to connect (or bridge) your “native” Java code implementations with HTML5 will be presented with code samples. The lecture will also cover the right and wrong ways to implement HTML5 in your application, and when to “stick to native.”
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized PlatformDatabricks
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Why APM Is Not the Same As ML MonitoringDatabricks
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixDatabricks
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks
In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature AggregationsDatabricks
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and SparkDatabricks
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks
Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta LakeDatabricks
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
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.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. Rafael Zotto, HP
Franco Vieira, HP
A Virtual Assistant
Ecosystem for Workflow and
Workplace Optimization
#UnifiedAnalytics #SparkAISummit
3. //ABOUT US
3#UnifiedAnalytics #SparkAISummit
//RAFAEL ZOTTO
Holds a master degree in Computer Science focused on high-performance
computing. Specialized in parallel and distributed computing with a special interest
in cloud and serverless computing. Works for HP Inc. for more than a decade acting
as a software engineer for print firmware and wearable technologies. Currently works
in the data science research team as a software engineer and solutions architect
having most activities related to applied AI and conversational interfaces.
//FRANCO VIEIRA
Professional with vast experience in information technology working in
software development, cloud computing, and machine learning projects. In
the past 5 years, Franco has been developing new services focused on activity
recognition, content sharing, and device health. Currently, works in HP
Personal Systems Software producing machine learning solutions on the edge.
Franco holds a degree in computer science.
11. //CHATBOT PLATFORM ANATOMY
11#UnifiedAnalytics #SparkAISummit
INTENTS UTTERANCES SLOTS
FULFILLMENT
COMPOSED
BY
MIGHT
HAVE
//EXAMPLE
_“I need to travel from San Francisco to Philadelphia next weekend”
SLOT 1: Origin SLOT 2: Destination SLOT 3: Date
12. //CHATBOT AS PART OF A SOLUTION
12#UnifiedAnalytics #SparkAISummit
//Our INSIGHTS and PREDICTIONS refers to a DOMAIN
//Our DOMAINS can be mapped to INTENTS
_Battery
_Thermal
_CPU
_…
//INTENTS can be fulfilled with INSIGHTS and PREDICTIONS
_MULTI-CONTEXT dialog management
13. //HIGH-LEVEL OVERVIEW
13#UnifiedAnalytics #SparkAISummit
ANALYTICS SERVICES
GRAPHQL
SAVES TO
WEB PORTAL
SLACK
CALL CENTER
MOBILE APP
INTENT
RECOGNITION
DESKTOP APP
LAMBDA
FUNCTION
FULFILLMENT
USES
_DYNAMIC response created
CONTEXT is created for the SESSION RDS
QUERY
14. //EASY access to our GOLD DATA
//UNDERSTAND user needs.
//DEMO
14#UnifiedAnalytics #SparkAISummit
15. //CREATING A PLATFORM
15#UnifiedAnalytics #SparkAISummit
ANALYTICS SERVICES
GRAPHQL
SAVES TO
WEB PORTAL
SLACK
CALL CENTER
MOBILE APP
INTENT
RECOGNITION
DESKTOP APP
LAMBDA
FUNCTION
FULFILLMENT
USES
RDS
QUERY
_Support for
MULTIPLE INTENTS
_Gathering data from
MULTIPLE SOURCES
16. //LEARN FROM MISSED UTTERANCES
16#UnifiedAnalytics #SparkAISummit
//UNDERSTAND what we still DON’T KNOW
MISSED UTTERANCES INPUT TEXT
ENTITIES KEY PHRASES
FILTERING SLOTS KNOWLEDGE NEEDED
INSIGHT DISCOVERY
INTENT RECOGNITION
//EXAMPLE
_“What was the health grade of my device fleet yesterday”
KNOWLEDGE : 0.99+ KNOWLEDGE: 0.99+ DATE: 0.98
17. //ONGOING WORK
_DEVICES are THINGS connected to our stack.
_We have INSIGHTS and PREDICTIONS ready to be used.
_Why not DELIVER them to the interested part?
17#UnifiedAnalytics #SparkAISummit
//IMMEDIATE delivery
_As soon as detected, NOTIFICATION is delivered to the USER.
//SCHEDULED delivery
_Kept to be delivered as a FUTURE or RECURRENT NOTIFICATION.
18. //DELIVERING INSIGHTS
18#UnifiedAnalytics #SparkAISummit
ANALYTICS SERVICES
EVENTS
NOTIFICATIONS ACTIONS
PRODUCE
TRIGGER
DELIVERED AS
_EXTENSIBLE list of PLUGINS
_ACT to address an issue
_WATCH the system
WRITES TO
EVENT SINK
WRITES TO
PROCESSING
INITIATE
SIMPLE QUEUE
MQTT