Observability versus Monitoring

Sri Chaganty

“Observability” has become a key trend in Service Reliability Engineering practice.  One of the recommendations from Gartner’s latest Market Guide for IT Infrastructure Monitoring Tools released in January 2020 says, “Contextualize data that ITIM tools collect from highly modular IT architectures by using AIOps to manage other sources, such as observability metrics from cloud-native monitoring tools.”

Like so many other terms in software engineering, ‘observability’ is a term borrowed from an older physical discipline: in this case, control systems engineering. Let me use the definition of observability from control theory in Wikipedia: “observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.”

Observability is gaining attention in the software world because of its effectiveness at enabling engineers to deliver excellent customer experiences with software despite the complexity of the modern digital enterprise.

When we blew up the monolith into many services, we lost the ability to step through our code with a debugger: it now hops the network.  Monitoring tools are still coming to grips with this seismic shift.

How is observability different than monitoring?

Monitoring requires you to know what you care about before you know you care about it. Observability allows you to understand your entire system and how it fits together, and then use that information to discover what specifically you should care about when it’s most important.

Monitoring requires you to already know what normal is. Observability allows discovery of different types of ‘normal’ by looking at how the system behaves, over time, in different circumstances.

Monitoring asks the same questions over and over again. Is the CPU usage under 80%? Is memory usage under 75% percent? Or, is the latency under 500ms? This is valuable information, but monitoring is useful for known problems.

Observability, on the other side, is about asking different questions almost all the time. You discover new things.

Observability allows the discovery of different types of ‘normal’ by looking at behavior, over time, in different circumstances.

Metrics do not equal observability.

What Questions Can Observability Answer?

Below are sample questions that can be addressed by an effective observability solution:

  • Why is x broken?
  • What services does my service depend on — and what services are dependent on my service?
  • Why has performance degraded over the past quarter?
  • What changed? Why?
  • What logs should we look at right now?
  • What is system performance like for our most important customers?”
  • What SLO should we set?
  • Are we out of SLO?
  • What did my service look like at time point x?
  • What was the relationship between my service and x at time point y?
  • What was the relationship of attributed across the system before we deployed? What’s it like now?
  • What is most likely contributing to latency right now? What is most likely not?
  • Are these performance optimizations on the critical path?

About the Author –

Sri is a Serial Entrepreneur with over 30 years’ experience delivering creative, client-centric, value-driven solutions for bootstrapped and venture-backed startups.

JAVA – Cache Management

Sivaprakash Krishnan

This article explores the offering of the various Java caching technologies that can play critical roles in improving application performance.

What is Cache Management?

A cache is a hot or a temporary memory buffer which stores most frequently used data like the live transactions, logical datasets, etc. This intensely improves the performance of an application, as read/write happens in the memory buffer thus reducing retrieval time and load on the primary source. Implementing and maintaining a cache in any Java enterprise application is important.

  • The client-side cache is used to temporarily store the static data transmitted over the network from the server to avoid unnecessarily calling to the server.
  • The server-side cache could be a query cache, CDN cache or a proxy cache where the data is stored in the respective servers instead of temporarily storing it on the browser.

Adoption of the right caching technique and tools allows the programmer to focus on the implementation of business logic; leaving the backend complexities like cache expiration, mutual exclusion, spooling, cache consistency to the frameworks and tools.

Caching should be designed specifically for the environment considering a single/multiple JVM and clusters. Given below multiple scenarios where caching can be used to improve performance.

1. In-process Cache – The In-process/local cache is the simplest cache, where the cache-store is effectively an object which is accessed inside the application process. It is much faster than any other cache accessed over a network and is strictly available only to the process that hosted it.

Data Center Consolidation Initiative Services

  • If the application is deployed only in one node, then in-process caching is the right candidate to store frequently accessed data with fast data access.
  • If the in-process cache is to be deployed in multiple instances of the application, then keeping data in-sync across all instances could be a challenge and cause data inconsistency.
  • An in-process cache can bring down the performance of any application where the server memory is limited and shared. In such cases, a garbage collector will be invoked often to clean up objects that may lead to performance overhead.

In-Memory Distributed Cache

Distributed caches can be built externally to an application that supports read/write to/from data repositories, keeps frequently accessed data in RAM, and avoid continuous fetching data from the data source. Such caches can be deployed on a cluster of multiple nodes, forming a single logical view.

  • In-memory distributed cache is suitable for applications running on multiple clusters where performance is key. Data inconsistency and shared memory aren’t matters of concern, as a distributed cache is deployed in the cluster as a single logical state.
  • As inter-process is required to access caches over a network, latency, failure, and object serialization are some overheads that could degrade performance.

2. In-memory database

In-memory database (IMDB) stores data in the main memory instead of a disk to produce quicker response times. The query is executed directly on the dataset stored in memory, thereby avoiding frequent read/writes to disk which provides better throughput and faster response times. It provides a configurable data persistence mechanism to avoid data loss.

Redis is an open-source in-memory data structure store used as a database, cache, and message broker. It offers data replication, different levels of persistence, HA, automatic partitioning that improves read/write.

Replacing the RDBMS with an in-memory database will improve the performance of an application without changing the application layer.

3. In-Memory Data Grid

An in-memory data grid (IMDG) is a data structure that resides entirely in RAM and is distributed among multiple servers.

Key features

  • Parallel computation of the data in memory
  • Search, aggregation, and sorting of the data in memory
  • Transactions management in memory
  • Event-handling

Cache Use Cases

There are use cases where a specific caching should be adapted to improve the performance of the application.

1. Application Cache

Application cache caches web content that can be accessed offline. Application owners/developers have the flexibility to configure what to cache and make it available for offline users. It has the following advantages:

  • Offline browsing
  • Quicker retrieval of data
  • Reduced load on servers

2. Level 1 (L1) Cache

This is the default transactional cache per session. It can be managed by any Java persistence framework (JPA) or object-relational mapping (ORM) tool.

The L1 cache stores entities that fall under a specific session and are cleared once a session is closed. If there are multiple transactions inside one session, all entities will be stored from all these transactions.

3. Level 2 (L2) Cache

The L2 cache can be configured to provide custom caches that can hold onto the data for all entities to be cached. It’s configured at the session factory-level and exists as long as the session factory is available.

  • Sessions in an application.
  • Applications on the same servers with the same database.
  • Application clusters running on multiple nodes but pointing to the same database.

4. Proxy / Load balancer cache

Enabling this reduces the load on application servers. When similar content is queried/requested frequently, proxy takes care of serving the content from the cache rather than routing the request back to application servers.

When a dataset is requested for the first time, proxy saves the response from the application server to a disk cache and uses them to respond to subsequent client requests without having to route the request back to the application server. Apache, NGINX, and F5 support proxy cache.

Desktop-as-a-Service (DaaS) Solution

5. Hybrid Cache

A hybrid cache is a combination of JPA/ORM frameworks and open source services. It is used in applications where response time is a key factor.

Caching Design Considerations

  • Data loading/updating
  • Performance/memory size
  • Eviction policy
  • Concurrency
  • Cache statistics.

1. Data Loading/Updating

Data loading into a cache is an important design decision to maintain consistency across all cached content. The following approaches can be considered to load data:

  • Using default function/configuration provided by JPA and ORM frameworks to load/update data.
  • Implementing key-value maps using open-source cache APIs.
  • Programmatically loading entities through automatic or explicit insertion.
  • External application through synchronous or asynchronous communication.

2. Performance/Memory Size

Resource configuration is an important factor in achieving the performance SLA. Available memory and CPU architecture play a vital role in application performance. Available memory has a direct impact on garbage collection performance. More GC cycles can bring down the performance.

3. Eviction Policy

An eviction policy enables a cache to ensure that the size of the cache doesn’t exceed the maximum limit. The eviction algorithm decides what elements can be removed from the cache depending on the configured eviction policy thereby creating space for the new datasets.

There are various popular eviction algorithms used in cache solution:

  • Least Recently Used (LRU)
  • Least Frequently Used (LFU)
  • First In, First Out (FIFO)

4. Concurrency

Concurrency is a common issue in enterprise applications. It creates conflict and leaves the system in an inconsistent state. It can occur when multiple clients try to update the same data object at the same time during cache refresh. A common solution is to use a lock, but this may affect performance. Hence, optimization techniques should be considered.

5. Cache Statistics

Cache statistics are used to identify the health of cache and provide insights about its behavior and performance. Following attributes can be used:

  • Hit Count: Indicates the number of times the cache lookup has returned a cached value.
  • Miss Count: Indicates number of times cache lookup has returned a null or newly loaded or uncached value
  • Load success count: Indicates the number of times the cache lookup has successfully loaded a new value.
  • Total load time: Indicates time spent (nanoseconds) in loading new values.
  • Load exception count: Number of exceptions thrown while loading an entry
  • Eviction count: Number of entries evicted from the cache

Various Caching Solutions

There are various Java caching solutions available — the right choice depends on the use case.

Software Test Automation Platform

At GAVS, we focus on building a strong foundation of coding practices. We encourage and implement the “Design First, Code Later” principle and “Design Oriented Coding Practices” to bring in design thinking and engineering mindset to build stronger solutions.

We have been training and mentoring our talent on cutting-edge JAVA technologies, building reusable frameworks, templates, and solutions on the major areas like Security, DevOps, Migration, Performance, etc. Our objective is to “Partner with customers to realize business benefits through effective adoption of cutting-edge JAVA technologies thereby enabling customer success”.

About the Author –

Sivaprakash is a solutions architect with strong solutions and design skills. He is a seasoned expert in JAVA, Big Data, DevOps, Cloud, Containers, and Micro Services. He has successfully designed and implemented a stable monitoring platform for ZIF. He has also designed and driven Cloud assessment/migration, enterprise BRMS, and IoT-based solutions for many of our customers. At present, his focus is on building ‘ZIF Business’ a new-generation AIOps platform aligned to business outcomes.

IoT Adoption during the Pandemic

Artificial Intelligence for IT Operations

Naveen KT

From lightbulbs to cities, IoT is adding a level of digital intelligence to various things around us. Internet of Things or IoT is physical devices connected to the internet, all collecting and sharing data, which can then be used for various purposes. The arrival of super-cheap computers and the ubiquity of wireless networks are behind the widespread adoption of IoT. It is possible to turn any object, from a pill to an airplane, into an IoT-enabled device. It is making devices smarter by letting them ‘sense’ and communicate, without any human involvement.

Let us look at the developments that enabled the commercialization of IoT.

History

The idea of integrating sensors and intelligence to basic objects dates to the 1980s and 1990s. But the progress was slow because the technology was not ready. Chips were too big and bulky and there was no way for an object to communicate effectively.

Processors had to be cheap and power-frugal enough to be disposed of before it finally becomes cost-effective to connect to billions of devices. The adoption of RFID tags and IPV6 was a necessary step for IoT to scale.

Kevin Ashton penned the phrase ‘Internet of Things’ in 1999. Although it took a decade for this technology to catch up with his vision. According to Ashton “The IoT integrates the interconnectedness of human culture (our things) with our digital information system(internet). That’s the IoT”.

Early suggestions for IoT include ‘Blogjects’ (object that blog and record data about themselves to the internet), Ubiquitous computing (or ‘ubicomp’), invisible computing, and pervasive computing.

How big is IoT?

AIOps in Infrastructure Management

IDC predicts that there will be 41.6 billion connected IoT devices by 2025. It also suggests industrial and automotive equipment represent the largest opportunity of connected ‘things’.

Gartner predicts that the enterprise and automotive sectors will account for 5.8 billion devices this year.

However, the COVID-19 pandemic has further enhanced the need for IoT-enabled devices to help the nations tackle the crisis.

IoT for the Government

Information about the movement of citizens is urgently required by governments to track the spread of the virus and potentially monitor their quarantine measures. Some IoT operators have solutions that could serve these purposes.

AIOps platform
  • Telia’s Division X has developed Crowd Insights which provides aggregated smartphone data to city and transport authorities of Nordic Countries. It is using the tool which will track the movement of citizens during the quarantine.
  • Vodafone provides insights on traffic congestion.
  • Telefonica developed Smart steps, which aggregates data on footfall and movement for the transport, tourism, and retail sectors.

Personal data of people will also help in tracking clusters of infection by changing the privacy regulations. For example, in Taiwan, high-risk quarantined patients were being monitored through their mobile phones to ensure compliance with quarantine rules. In South Korea, the officials track infected citizens and alert others if they come into contact with them. The government of Israel went as far as passing an emergency law to monitor the movement of infected citizens via their phones.

China is already using mass temperature scanning devices in public areas like airports. A team of researchers at UMass Amherst is testing a device that can analyze coughing sounds to identify the presence of flu-like symptoms among crowds.

IoT in Health care

COVID-19 could be the trigger to explore new solutions and be prepared for any such future pandemics, just as the SARS epidemic in 2003 which spurred the governments in South Korea and Taiwan to prepare for today’s problems.

IT operations analytics

Remote patient monitoring (RPM) and telemedicine could be helpful in managing a future pandemic. For example, patients with chronic diseases who are required to self-isolate to reduce their exposure to COVID-19 but need continuous care would benefit from RPM. Operators like Orange, Telefónica, and Vodafone already have some experience in RPM.

Connected thermometers are being used in hospitals to collect data while maintaining a social distance. Smart wearables are also helpful in preventing the spread of the virus and responding to those who might be at risk by monitoring their vital signs.

Connected thermometers are being used in hospitals to collect data while maintaining a social distance. Smart wearables are also helpful in preventing the spread of the virus and responding to those who might be at risk by monitoring their vital signs.

Telehealth is widely adopted in the US, and the authorities there are relaxing reimbursement rules and regulations to encourage the extension of specific services. These include the following.

  • Medicare, the US healthcare program for senior citizens, has temporarily expanded its telehealth service to enable remote consultations.
  • The FCC has made changes to the Rural Health Care (RHC) and E-Rate programs to support telemedicine and remote learning. Network operators will be able to provide incentives or free network upgrades that were previously not permitted, for example, for hospitals that are looking to expand their telemedicine programs.

IoT for Consumers

The IoT promises to make our environment smarter, measurable, and interactive.COVID-19 is highly contagious, and it can be transmitted from one to another even by touching the objects used by the affected person. The WHO has instructed us to disinfect and sanitize high touch objects. IoT presents us with an ingenious solution to avoid touching these surfaces altogether. Hands-free and sensor-enabled devices and solutions like smart lightbulbs, door openers, smart sinks, and others help prevent the spread of the virus.

Security aspects of IoT

Security is one of the biggest issues with the IoT. These sensors collect extremely sensitive data like what we say and do in our own homes and where we travel. Many IoT devices lack security patches, which means they are permanently at risk. Hackers are now actively targeting IoT devices such as routers and webcams because of their inherent lack of security makes them easy to compromise and pave the way to giant botnets.

Machine learning service provider
Machine learning service provider

IoT bridges the gap between the digital and the physical world which means hacking into devices can have dangerous real-world consequences. Hacking into sensors and controlling the temperature in power stations might end up in catastrophic decisions and taking control of a driverless car could also end in disaster.

Overall IoT makes the world around us smarter and more responsive by merging the digital and physical universe. IoT companies should look at ways their solutions can be repurposed to help respond to the crisis.

Enterprise IT infrastructure services
Enterprise IT infrastructure services

References:

  • https://www.analysysmason.com/Research/Content/Comments/covid19-iot-role-rdme0-rma17/
  • shorturl.at/wBFGT

Naveen is a software developer at GAVS. He teaches underprivileged children and is interested in giving back to society in as many ways as he can. He is also interested in dancing, painting, playing keyboard, and is a district-level handball player.

Combating a health crisis with digital health technologies

Bindu Vijayan

The current pandemic has exposed yawning gaps in the systems of the best of developed countries to be able to respond to virulent pathogens.  The world has seen SARS and Ebola in fairly recent times, and with the COVID 19 pandemic, it is becoming clear that technology can help combat and overcome future epidemics if we plan and strategize with these technologies.  They bring efficiency to our response times, and we are currently learning the importance of using these technologies for prevention as well.  A small example – Canadian AI health monitoring platform BlueDot’s outbreak risk software is said to have predicted the outbreak of the pandemic a whole week before America (who announced on Jan 8), and the WHO (on Jan 9) did. BlueDot predicted the spread of COVID 19 from Wuhan to other countries like Bangkok and Seoul by parsing through huge volumes of international news (in local languages).  It further was able to predict where the infection would spread by accessing global airline data to trace and track where the infected people were headed.

Contrary to earlier times, today it only takes a few hours to sequence a virus, thanks of course, to technology.  The scientists don’t have to cultivate a sufficient batch of viruses any longer in order to examine them, today, its DNA can be got from an infected person’s blood sample or saliva.  India’s National Institute of Animal Biotechnology (NIAB), Hyderabad, has developed a biosensor that can detect the novel coronavirus in saliva samples. The new portable device called ‘eCovSens’, can detect coronavirus antigens in human saliva within 30 seconds using just 20 microlitres of sample.  Startups like Canadian GenMarkDx, US-based Aperiomics & XCR Diagnostics, Singapore based MiRXES, and Polish company’s SensDx have introduced top notch diagnostic solutions.  Identifying infected people to provide strict medical care will be made a lot faster with these diagnostic kits. 

Genome sequencing is also vital to fight the pandemic.  The genome of this virus was completely sequenced by the Chinese scientists in under a month from detection of the first case, and then on the biotech companies created synthetic copies of the virus for research.  Today creating a synthetic copy of a single nucleotide costs under 10 cents (in comparison to the earlier $ 10), so these days it is far quicker and cheaper, which means the chances of finding appropriate / adequate medication are much faster which will help save more lives.

Healthcare workers are having to pay a huge price, they run the risk of getting infected, there is often paucity of PPE, and in some countries, they even have to face assault from crowds that are angry and confused at the situation.  Medical workers are targetted by mobs, there are instances where communities don’t allow them to come back to their homes after duty, shops don’t sell them necessities, etc.  Medical robots can be the real game-changers in such situations.  Deploying robots in such scenarios to do the rescue is becoming a much sought after option, wherever possible.   Robots become the answer to such difficult situations as they are impervious to infections.  They allow physicians to treat/communicate through a screen. The patient’s vitals are also recorded by the robot.  Patients can be very efficiently monitored this way.

Drones for deliveries, especially medical deliveries can also be used to reach isolation zones or quarantined zones.  Italy made a big success out of this. Italy’s coronavirus epicenter, Bergamo, in Lombardy region, had to resort to people’s temperature being read by drones.  ‘The Star’ reported that “once a person’s temperature is read by the drone, you must still stop that person and measure their temperature with a normal thermometer,” said Matteo Copia, a police commander in Treviolo, near Bergamo. Drones are being used for surveillance – In areas where people were not complying with social distancing and lockdown restrictions, authorities are using drones to monitor people’s movement and break up social gatherings that could be a potential risk to the society. Drones are also being used for Disinfectant spraying, broadcasting messages, medicine and grocery deliveries and so on.

Interactive maps give us the data on the pandemic on real time, and monitoring a pandemic this wide and dangerous is very crucial to stopping/controlling its spread. These maps are made available to everybody, and the truth and transparency in the situation of such epic proportion is necessary in order to avoid panic within communities.  We now have apps for tracking the virus spread, fatalities and recovery rates, and apps would be developed for the future that will warn us about impending outbreaks, the geographies and flight routes that we must avoid

Implementing these technologies will enable us to manage and conquer situations like the current pandemic we are going through. As Bernardo Mariano Junior, Director of WHO’s Department of Digital Health and Innovation, rightly said “The world needs to be well prepared and united in the spirit of shared responsibility, to digitally detect, protect, respond, and prepare the recovery for COVID 19. No single entity or single country initiative will be sufficient. We need everyone.”

References:

Smart Spaces Tech Trends for 2020

data center as a service providers in usa

Priyanka Pandey

These are unprecedented times. The world hadn’t witnessed such a disruption in recent history. It is times like these test the strength and resilience of our community. While we’ve been advised to maintain social distancing to flatten to curve, we must keep the wheels of the economy rolling.

In my previous article, I covered the ‘People-Centric’ Tech Trends of the year, i.e., Hyper automation, Multiexperience, Democratization, Human Augmentation and Transparency and Traceability. All of those hold more importance now in the light of current events. Per Gartner, Smart Spaces enable people to interact with people-centric technologies. Hence, the next Tech Trends in the list are about creating ‘Smart Spaces’ around us.

Smart spaces, in simple words, are interactive physical environments decked out with technology, that act as a bridge between humans and the digital world. The most common example of a smart space is a smart home, also called as a connected home. Other environments that could be a smart space are offices and communal workspaces; hotels, malls, hospitals, public places such as libraries and schools, and transportation portals such as airports and train stations. Listed below are the 5 Smart Spaces Technology Trends which, per Gartner, have great potential for disruption.

Trend 6: Empowered Edge

Edge computing is a distributed computing topology in which information processing and data storage are located closer to the sources, repositories and consumers of this information. Empowered Edge is about moving towards a smarter, faster and more flexible edge by using more adaptive processes, fog/mesh architectures, dynamic network topology and distributed cloud. This trend will be introduced across a spectrum of endpoint devices which includes simple embedded devices (e.g., appliances, industrial devices), input/output devices (e.g., speakers, screens), computing devices (e.g., smartphones, PCs) and complex embedded devices (e.g., automobiles, power generators). Per Gartner predictions, by 2022, more than 50% of enterprise-generated data will be created and processed outside the data center or cloud. This trend also includes the next-generation cellular standard after 4G Long Term Evolution (LTE), i.e., 5G. The concept of edge also percolates to the digital-twin models.

Trend 7: Distributed Cloud

Gartner defines a distributed cloud as “distribution of public cloud services to different locations outside the cloud providers’ data centers, while the originating public cloud provider assumes responsibility for the operation, governance, maintenance and updates.” Cloud computing has always been viewed as a centralized service, although, private and hybrid cloud options compliments this model. Implementing private cloud is not an easy task and hybrid cloud breaks many important cloud computing principles such as shifting the responsibility to cloud providers, exploiting the economics of cloud elasticity and using the top-class services of large cloud service providers. A distributed cloud provides services in a location which meets organization’s requirements without compromising on the features of a public cloud. This trend is still in the early stages of development and is expected to build in three phases:

Phase 1: Services will be provided from a micro-cloud which will have a subset of services from its centralized cloud.

Phase 2: An extension to phase 1, where service provider will team up with a third-party to deliver subset of services from the centralized cloud.

Phase 3: Distributed cloud substations will be setup which could be shared by different organizations. This will improve the economics associated as the installation cost can be split among the companies.

Trend 8: Autonomous Things

Autonomous can be defined as being able to control oneself. Similarly, Autonomous Things are devices which can operate by themselves without human intervention using AI to automate all their functions. The most common among these devices are robots, drones, and aircrafts. These devices can operate across different environments and will interact more naturally with their surroundings and people. While exploring use cases of this technology, understanding the different spaces the device will interact to, is very important like the people, terrain obstacles or other autonomous things. Another aspect to consider would be the level of autonomy which can be applied. The different levels are: No automation, Human-assisted automation, Partial automation, Conditional automation, High automation and Full automation. With the proliferation of this trend, a shift is expected from stand-alone intelligent things to collaborative intelligent things in which multiple devices work together to deliver the final output. The U.S. Defense Advanced Research Projects Agency (DARPA) is studying the use of drone swarms to defend or attack military targets.

Trend 9: Practical Blockchain

Most of us have heard about Blockchain technology. It is a tamper-proof, decentralized, distributed database that stores blocks of records linked together using cryptography. It holds the power to take industries to another level by enabling trust, providing transparency, reducing transaction settlement times and improving cash flow. Blockchain also makes it easy to trail assets back to its origin, reducing the chances of substituting it with counterfeit products. Smart contracts are used as part of the blockchain which can trigger actions on encountering any change in the blockchain; such as releasing payment when goods are received. New developments are being introduced in public blockchains but over time these will be integrated with permissioned blockchains which supports membership, governance and operating model requirements. Some of the use cases of this trend that Gartner has identified are: Asset Tracking, Identity Management/Know Your Client (KYC), Internal Record Keeping, Shared Record Keeping, Smart Cities/the IoT, Trading, Blockchain-based voting, Cryptocurrency payments and remittance services. Per the 2019 Gartner CIO Survey, in the next three years 60% of CIOs expect blockchain deployment in some way.

Trend 10: AI Security

Per Gartner, over the next five years AI-based decision-making will be applied across a wide set of use cases which will result in a tremendous increase of potential attack surfaces. Gartner provides three key perspectives on how AI impacts security: protecting AI-powered systems, leveraging AI to enhance security defense and anticipating negative use of AI by attackers. ML pipelines have different phases and at each of these phases there are various kinds of risks associated. AI-based security tools can be very powerful extension to toolkits with use cases such as security monitoring, malware detection, etc. On the other hand, there are many AI-related attack techniques which include training data poisoning, adversarial inputs and model theft and per Gartner predictions, through 2022, 30% of all AI cyberattacks will leverage these attacking techniques. Every innovation in AI can be exploited by attackers for finding new vulnerabilities. Few of the AI attacks that security professionals must explore are phishing, identity theft and DeepExploit.

One of the most important things to note here is that the trends listed above cannot exist in isolation. IT leaders must analyse what combination of these trends will drive the most innovation and strategy fitting it into their business models. Soon we will have smart spaces around us in forms of factories, offices and cities with increasingly insightful digital services everywhere for an ambient experience.

Sources:

https://www.pcmag.com/news/gartners-top-10-strategic-technology-trends-for-2020

About the Author:

Priyanka is an ardent feminist and a dog-lover. She spends her free time cooking, reading poetry and exploring new ways to conserve the environment.

Discover, Monitor, Analyze & Predict COVID-19

Bargunan Somasundaram

Uber, the world’s largest taxi company, owns no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. Netflix, the world’s largest movie house, own no cinemas. And Airbnb, the world’s largest accommodation provider, owns no real estate. Something interesting is happening.”

– Tom Goodwin, an executive at the French media group Havas.

This new breed of companies is the fastest growing in history because they own the customer interface layer. It is the platform where all the value and profit is. “Platform business” is a more wholesome term for this model for which data is the fuel; Big Data & AI/ML technologies are the harbinger of new waves of productivity growth and innovation.

With Big data and AI/ML is making a big difference in the area of public health, let’s see how it is helping us tackle the global emergency of coronavirus formally known as COVID-19.

DISCOVERING / DETECTING

Chinese technology giant Alibaba has developed an AI system for detecting the COVID-19 in CT scans of patients’ chests with 96% accuracy against viral pneumonia cases. It only takes 20 seconds for the AI to decide, whereas humans generally take about 15 minutes to diagnose the illness as there can be upwards of 300 images to evaluate. The system was trained on images and data from 5,000 confirmed coronavirus cases and has been tested in hospitals throughout China. Per a report, at least 100 healthcare facilities are currently employing Alibaba’s AI to detect COVID-19.

Ping An Insurance (Group) Company of China, Ltd (Ping An) aims to address the issue of lack of radiologists by introducing the COVID-19 smart image-reading system. This image-reading system can read the huge volumes of CT scans in epidemic areas.

Ping An Smart Healthcare uses clinical data to train the AI model of the COVID-19 smart image-reading system. The AI analysis engine conducts a comparative analysis of multiple CT scan images of the same patient and measures the changes in lesions. It helps in tracking the development of the disease, evaluation of the treatment and in prognosis of patients. Ultimately it assists doctors to diagnose, triage and evaluate COVID-19 patients swiftly and effectively.

Ping An Smart Healthcare’s COVID-19 smart image-reading system also supports AI image-reading remotely by medical professionals outside the epidemic areas. Since its launch, the smart image-reading system has provided services to more than 1,500 medical institutions. More than 5,000 patients have received smart image-reading services for free.

The more solutions the better. At least when it comes to helping overwhelmed doctors provide better diagnoses and, thus, better outcomes.

MONITORING

  • AI based Temperature monitoring & scanning

In Beijing, China, subway passengers are being screened for symptoms of coronavirus, but not by health authorities. Instead, artificial intelligence is in-charge.

Two Chinese AI giants, Megvii and Baidu, have introduced temperature-scanning. They have implemented scanners to detect body temperature and send alerts to company workers if a person’s body temperature is high enough to constitute a fever.

Megvii’s AI system detects body temperatures for up to 15 people per second and up to 16 feet. It monitors as many as 16 checkpoints in a single station. The system integrates body detection, face detection, and dual sensing via infrared cameras and visible light. The system can accurately detect and flag high body temperature even when people are wearing masks, hats, or covering their faces with other items. Megvii’s system also sends alerts to an on-site staff member.

Baidu, one of the largest search-engine companies in China, screens subway passengers at the Qinghe station with infrared scanners. It also uses a facial-recognition system, taking photographs of passengers’ faces. If the Baidu system detects a body temperature of at least 99-degrees Fahrenheit, it sends an alert to the staff member for another screening. The technology can scan the temperatures of more than 200 people per minute.

  • AI based Social Media Monitoring

An international team is using machine learning to scour through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs of the virus across geographies. The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel.

The approach has proven capable of spotting a coronavirus needle in a haystack of big data. This technique could help experts learn how the virus behaves. It may be possible to determine the age, gender, and location of those most at risk quicker than using official medical sources.

PREDICTING

Data from hospitals, airports, and other public locations are being used to predict disease spread and risk. Hospitals can also use the data to plan for the impact of an outbreak on their operations.

Kalman Filter

Kalman filter was pioneered by Rudolf Emil Kalman in 1960, originally designed and developed to solve the navigation problem in the Apollo Project. Since then, it has been applied to numerous cases such as guidance, navigation, and control of vehicles, computer vision’s object tracking, trajectory optimization, time series analysis in signal processing, econometrics and more.

Kalman filter is a recursive algorithm which uses time-series measurement over time, containing statistical noise and produce estimations of unknown variables.

IT Infrastructure Managed Services

For the one-day prediction Kalman filter can be used, while for the long-term forecast a linear model is used where its main features are Kalman predictors, infected rate relative to population, time-depended features, and weather history and forecasting.

The one-day Kalman prediction is very accurate and powerful while a longer period prediction is more challenging but provides a future trend. Long term prediction does not guarantee full accuracy but provides a fair estimation following the recent trend. The model should re-run daily to gain better results.

GitHub Link: https://github.com/Rank23/COVID19

ANALYZING

The Center for Systems Science and Engineering at Johns Hopkins University has developed an interactive, web-based dashboard that tracks the status of COVID-19 around the world. The resource provides a visualization of the location and number of confirmed COVID-19 cases, deaths and recoveries for all affected countries.

The primary data source for the tool is DXY, a Chinese platform that aggregates local media and government reports to provide COVID-19 cumulative case totals in near real-time at the province level in China and country level otherwise. Additional data comes from Twitter feeds, online news services and direct communication sent through the dashboard. Johns Hopkins then confirms the case numbers with regional and local health departments. This kind of Data analytics platform plays a pivotal role in addressing the coronavirus outbreak.

All data from the dashboard is also freely available in the following GitHub repository.

GitHub Link: https://bit.ly/2Wmmbp8

Mobile version: https://bit.ly/2WjyK4d

Web version: https://bit.ly/2xLyT6v

Conclusion

One of AI’s core strengths when working on identifying and limiting the effects of virus outbreaks is its incredibly insistent nature. AI systems never tire, can sift through enormous amounts of data, and identify possible correlations and causations that humans can’t.

However, there are limits to AI’s ability to both identify virus outbreaks and predict how they will spread. Perhaps the best-known example comes from the neighboring field of big data analytics. At its launch, Google Flu Trends was heralded as a great leap forward in relation to identifying and estimating the spread of the flu—until it underestimated the 2013 flu season by a whopping 140 percent and was quietly put to rest. Poor data quality was identified as one of the main reasons Google Flu Trends failed. Unreliable or faulty data can wreak havoc on the prediction power of AI.

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About the Author:

Bargunan is a Big Data Engineer and a programming enthusiast. His passion is to share his knowledge by writing his experiences about them. He believes “Gaining knowledge is the first step to wisdom and sharing it is the first step to humanity.”