Machine Learning

Temporal and Sequential Pattern Discovery

Learn about temporal and sequential pattern discovery in this article by Rahul Kumar, a seasoned professional in the area of business consulting and business problem solving, fuelled by his proficiency in machine learning and deep learning.

Many of us have visited retail shops such as Reliance and Walmart for our household needs. Let's say that we are planning to buy an iPhoneX from Reliance Digital. What we would typically do is search for the model by visiting the mobile section of the store and then select the product and head toward the billing counter.

But, in today's world, the goal of the organization is to increase revenue. Can this be done by pitching just one product at a time to the customer? The answer is a clear no. Hence, organizations began mining data relating to frequently bought items. They try to find out associations between different items and products that can be sold together, which gives assisting in right product placement. Typically, it figures out what products are being bought together and organizations can place products in a similar manner.

This is what we are going to talk about in this article. How do we come up with such rules by means of machine learning? We will discuss number of techniques here:

  • Association rules
  • Frequent pattern growth
  • Validation

Association rules

Association rule mining is a technique that focuses upon observing frequently occurring patterns and associations from datasets found in databases such as relational and transactional databases. These rules do not say anything about the preferences of an individual; rather, they rely chiefly on the items within transactions to deduce a certain association. Every transaction is identified by a primary key (distinct ID) called transaction ID. All these transactions are studied as a group and patterns are mined.

Association rules can be thought of as an if—then relationship. Just to elaborate on that we have to come up with a rule: if an item A is being bought by the customer, then the chances of item B being picked by the customer too under the same transaction ID (along with item A) is found out. You need to understand here that it's not causality; rather, it is co-occurrence pattern that comes to the fore.

There are two elements of these rules:

  • Antecedent (if): This is an item/group of items that are typically found in the itemsets or datasets
  • Consequent (then): This comes along as an item with an antecedent/group of antecedents

Have a look at the following rule:

{Bread, milk} {Butter}

The first part of this rule is called antecedent and the second part (after the arrow) is consequent. It is able to convey that there is a chance of Butter being picked in a transaction if Bread and Milk are picked earlier. However, the percentage chance for the consequent to be present in an itemset, given the antecedent, is not clear.

Let's look at a few metrics that will help us get there:

  1. Support: This is a measure of the frequency of the itemset in all the transactions. For example, there are two itemsets popping up through the number of transactions for a retail outlet such as Walmart: itemset A = {Milk}, itemset B = {laptop}. Given that support is how frequent the itemset is in all the transactions, we are asked to find out which itemset has got the higher support. We know that itemset A will have higher support because Milk features in everyday grocery lists (and, in turn, the transaction) at a greater probability than laptop. Let's add another level of association and study with two new itemsets: itemset A= {milk, cornflakes}, itemset B= {milk, USB Drive}. The purchasing frequency of milk and cornflakes together will be higher than milk and USB Drive. It will make the support metric higher for A.

Let's translate this into mathematics:

Support(A, B) = Transactions comprising A and B/Total number of transactions

Here's an example:

  • The total number of transactions is 10,000
  • Transactions comprising A and B = 500
  • Then, support (A, B) = 500/10000= 0.05
  • 5% of transactions contain A and B together

 

  1. Confidence: This indicates how likely item 1 is to be purchased/picked when item 2 is already picked. In other words, it measures the likelihood of the occurrence of consequent transactions given that the antecedent is already there in the transaction. In other words, it is the probability of the occurrence of Butter in the transaction if Bread has already been part of that transaction. It is quite clear that it is a conditional probability of the occurrence of the consequent while having the antecedent:
  • Confidence(A B) = Transactions comprising A and B/Transactions comprising A
  • Confidence can be transformed in terms of support
  • Confidence(A B) = Support(A, B)/Support(A)

 Here's an example:

  • Transactions with the itemset as milk = 50
  • Transactions with the itemset as cereal = 30
  • Transactions comprising milk and cereal = 10
  • Total number of transactions = 100
  • Confidence(milk Cereal) = 10/(50 +10) = 0.167

It means that there is 16.7% probability of that event taking place.

A drawback of the confidence is it only accounts for how popular item 1 is, but not item 2. If item 2 is equally frequent, there will be a higher chance that a transaction containing item 1 will also contain item 2. Hence, it will result in an inflated outcome. To account for the frequency of both constituent items, we use a third measure called lift.

  1. Lift: This is an indicator of how likely it is that item B will be picked in the cart/transaction; given that item A is already picked, while keeping a tab on the frequency of item B. A lift value greater than 1 says that there is a great association between item A and item B, which implies that there is a good chance that item B will be picked if item A is already in the cart. A lift value of less than 1 means that the chances are slim that item B will be picked if item A is already present. If the lift value hits zero, it means no association can be established here.

Lift(AB) = (Transactions comprising A and B/(Transactions comprising A))/fraction of Transaction comprising B

Implies:

= Support(A, B)/(Support(A) * Support(B))

Lift(milkcereal) = ( 10/(50+10))/0.4

= 0.416

We will see this in a better format here. The probability of having cereal in the cart with the knowledge that milk is already in the cart (which is called confidence) = 10/(50+10) = 0.167.

The probability of having cereal in the cart without the knowledge that milk is in the cart = (30+10)/100 = 0.4.

It means that having knowledge that milk is already in the cart reduces the chance of picking cereal from 0.4 to 0.167. It is a lift of 0.167/0.4= 0.416 and is less than 1. Hence, the chances of picking cereal while milk is already in the cart are very small.

Apriori algorithm

Apriori is a classical algorithm that is used to mine frequent itemsets to derive various association rules. It will help set up a retail store in a much better way, which will aid revenue generation.

The anti-monotonicity of the support measure is one of the prime concepts around which Apriori revolves. It assumes the following:

  • All subsets of a frequent itemset must be frequent
  • Similarly, for any infrequent itemset, all its supersets must be infrequent too

Let's look at an example and explain it:

Transaction ID

Milk

Butter

Cereal

Bread

Book

t1

1

1

1

0

0

t2

0

1

1

1

0

t3

0

0

0

1

1

t4

1

1

0

1

0

t5

1

1

1

0

1

t6

1

1

1

1

1

We have got the transaction ID and items such as milk, butter, cereal, bread, and book. 1 denotes that item is part of the transaction and 0 means that it is not.

  • We came up with a frequency table for all the items along, with support (division by 6):

Items

Number of transactions

Support

Milk

4

67%

Butter

5

83%

Cereal

4

67%

Bread

4

67%

Book

3

50%

  • We will put a threshold of support at 60%, which will filter out the items by frequency as these are the ones that can be addressed as frequent itemsets in this scenario:

Items

Number of transactions

Milk

4

Butter

5

Cereal

4

Bread

4

  • Similarly, we form the number of combinations (two at a time, three at a time, and four at a time) with these items and find out frequencies:

Items

Number of transactions

Milk, Butter

4

Milk, Cereal

3

Milk, Bread

2

Butter, Bread

3

Butter, Cereal

4

Cereal, Bread

2

Now, again, we have to find out the support for the preceding examples and filter them by threshold, which is support at 60%.

Similarly, the combinations have to be formed with three items at a time (for example, Milk, Butter, and Bread) and support needs to be calculated for them. And, finally, we will filter them out by threshold. The same process needs to be done by doing four items at a time. The step that we have done till now is called frequent itemset generation.

Continue Reading this Article: Machine Learning Quick Reference Part-2

Machine learning (ML) is an amazing subfield of Artificial Intelligence (AI) that tries to mimic the learning behavior of humans. Similar to the way a baby learns by observing the examples it encounters, an ML algorithm learns the outcome or response to a future incident by observing the data points that are provided as input to it. This has caused a major disruption in the way traditional software engineering functions, with most people transitioning from traditional software engineering practice to ML. This article takes a look at the differences between the two.

ML versus software engineering

Superficially, both ML and software engineering seem to generate some sort of code to perform a particular task. An interesting fact to observe is that, unlike software engineering where a programmer explicitly writes a program with various responses based on several conditions, the ML algorithm infers the rules of the game by observing the input examples. The rules that are learned are further used for better decision making when new input data is fed to the system.

As you can observe in the following diagram, automatically inferring the actions from data without manual intervention is the key differentiator between ML and traditional programming:

 

Another key differentiator of ML from traditional programming is that the knowledge acquired through ML can generalize beyond the training samples by successfully interpreting data that the algorithm has never seen before. Whereas, a program coded in traditional programming can only perform the responses that were included as part of the code.

Yet another differentiator is that in software engineering, there are certain specific ways to solve a problem at hand. Given an algorithm developed based on certain assumptions of inputs and the conditions incorporated, you will be able to guarantee the output that will be obtained given an input.

In the ML world, it is not possible to provide such assurances on the output obtained from the algorithms. It is also very difficult in the ML world to confirm if a particular technique is better than another without actually trying both the techniques on the dataset for the problem at hand.

While there is more than one formal definition that exists for ML, the following mentioned are a few key definitions encountered often:

"Machine learning is the science of getting computers to act without being explicitly programmed." – Stanford

"Machine learning is based on algorithms that can learn from data without relying on rules-based programming." – McKinsey and Co.

With the rise of data as the fuel of the future, the terms AI, ML, data mining, data science, and data analytics are used interchangeably by industry practitioners. It is important to understand the key differences between these terms to avoid confusion:

  • AI: AI is a paradigm where machines are able to perform tasks in a smart way. It may be observed that in the definition of AI, it is not specified whether the smartness of machines may be achieved manually or automatically. Therefore, it is safe to assume that even a program written with several ..elseor switch...case statements that has then been infused with a machine to carry out tasks may be considered to be AI.
  • ML: ML, on the other hand, is a way for the machine to achieve smartness by learning from the data that is provided as input and, thereby, we have a smart machine performing a task. It may be observed that ML achieves the same objective of AI except that the smartness is achieved automatically. Therefore, it can be concluded that ML is simply a way to achieve AI.
  • Data mining: Data mining is a specific field that focuses on discovering the unknown properties of the datasets. The primary objective of data mining is to extract rules from large amounts of data provided as input, whereas in ML, an algorithm not only infers rules from the data input, but also uses the rules to perform predictions on any new, incoming data.
  • Data analytics: Data analytics is a field that encompasses performing fundamental descriptive statistics, data visualization, and data points communication for conclusions. Data analytics may be considered to be a basic level within data science. It is normal for practitioners to perform data analytics on the input data provided for data mining or ML exercises. Such analysis on data is generally termed as exploratory data analysis (EDA).
  • Data science: Data science is an umbrella term that includes data analytics, data mining, ML, and any specific domain expertise pertaining to the field of work. Data science is a concept that includes several aspects of handling the data such as acquiring the data from one or more sources, data cleansing, data preparation, and creating new data points based on existing data. It includes performing data analytics. It also encompasses using one or more data mining or ML techniques on the data to infer knowledge to create an algorithm that performs a task on unseen data. This concept also includes deploying the algorithm in a way that it is useful to perform the designated tasks in the future.

The following is a Venn diagram which demonstrates the skills required by a professional working in the data science ambit. It has three circles, each of which defines a specific skill that a data science professional should have:

 

Let's explore the skills mentioned in the above diagram:

  • Math & Statistic Knowledge: This skill is required to analyze the statistical properties of the data.
  • Hacking Skills: Programming skills play a key role in order to process the data in a quick manner. The ML algorithm is applied to create an output that will perform the prediction on unseen data.
  • Substantive Expertise: This skill refers to the domain expertise in the field of the problem at hand. It helps the professional to be able to provide proper inputs to the system from which it can learn and to assess the appropriateness of the inputs and results obtained.

As we can see, AI, data science, data analytics, data mining, and ML are all interlinked. All of these areas are the most in-demand domains in the industry right now. The right skill sets in combination with real-world experience will lead to a strong career in these areas which are currently trending.

ML is everywhere! Most of the time, we may be using something that is ML-based but don’t realize its existence or the influence that it has on our lives! Let's explore some very popular devices or applications that we experience on a daily basis, which are powered by ML:

  • Virtual personal assistants(VPAs) such as Google AlloAlexaGoogle NowGoogle HomeSiri, and so on 
  • Smart maps that show you traffic predictions, given your source and destination
  • Demand-based price surging in Uber or similar transportation services
  • Automated video surveillance in airports, railway stations, and other public places
  • Face recognition of individuals in pictures posted on social media sites such as Facebook
  • Personalized news feeds served to you on Facebook
  • Advertisements served to you on YouTube
  • People you may knowsuggestions on Facebook and other similar sites
  • Job recommendations on LinkedIn, based on your profile
  • Automated responses on Google Mail
  • Chatbots that you converse with in online customer support forums
  • Search engine results filtering
  • Email spam filtering

Of course, the list does not end here. The above applications are just a few of the basic ones that illustrate the influence that ML has on our lives today. It is not astonishing to quote that there is no subject area that ML has not touched!

The topics in this section are by no means an exhaustive description of ML. If you’d want to have an in-depth look at implementing ML to build intelligent applications, you must check out R Machine Learning Projects. Following a project-based approach, R Machine Learning Projects takes the readers through the underlying concepts step by step, to help them master the different domains of machine learning like Reinforcement Learning, RNNs, CNNs, and more using R ecosystem.  

 

Today, organizations are swimming in a sea of data. Where is it coming from? Data is flowing in from social media, IoT sensors, email, and video, call center interactions, internet browsing—you name it. It seems everything is creating data.

The problem: “Knowledge is neither data nor information,” as Tom Davenport and Larry Prusak famously noted years ago in their book Working Knowledge. Fortunately, there are companies focused on turning data and information into actionable insight and knowledge that can have real business value.

To help shine a light on innovative knowledge management vendors, each year KMWorld presents the 100 Companies That Matter in Knowledge Management, a list compiled over the course of the past 12 months. The 2019 KMWorld 100 list spans a wide variety of companies that are each addressing the evolving demands of knowledge management. Some are long-standing companies with well-established offerings that have evolved over time, while others are much more recent entrants to the field.

Today’s knowledge management products and services offered by leading companies put a high priority on getting information to users when and where they need it, while also keeping it safe from unauthorized access. Many also include newer capabilities such as AI, machine learning, natural language processing, and digital assistants as well as choices of on-premise or cloud deployment—or a combination of both.

In selecting organizations to be included on the list, we consider insights gleaned from our own interactions with companies during interviews and events—how they have succeeded in helping customers solve business problems, and we review product updates to make sure that capabilities are advancing to address evolving requirements.

Technologies evolve and so does this list. Join us as we continue to explore a broad range of knowledge management topics and technologies in person at the annual KMWorld conference, which will return to the JW Marriott in Washington, D.C., November 5–7. And, to stay on top of the latest knowledge management news, trends, and research, be sure to go to www.kmworld.com.
 

VIEW FROM THE TOP

We encourage you to visit the websites of the companies on this year’s list. Also, in the View From the Top section, you can hear from some of the companies. CEOs and other top officials share their opinions on the state of the knowledge economy and how their solutions help customers realize their business goals.


  • Accenture global professional services company, Accenture works at the intersection of business and technology to drive innovation with investments in machine-led solutions, skilled data specialists, and leading platform and industry data capabilities to help clients rotate to data-powered intelligent enterprises.
  • Access InnovationsSeeking to give customers power over their data, Access Innovations provides a bespoke taxonomy-—or the tools and the knowledge to create their own—and also delivers software to make implementing semantic technologies for knowledge management use cases easier. To Learn more, read Heather Kotula, VP Marketing and Communications' View from the Top.
  • AccusoftOffering APIs and software development toolkits built using patented technology, Accusoft enables high-performance document viewing, advanced search, image compression, conversion, barcode recognition, OCR, and other image processing tools for use in application and web development.
  • AcquiaFocused on the digital experience and open source innovation, Acquia provides brands with technology that allows them to create customer moments that matter while also providing customers with the freedom to build tomorrow on their terms.     
  • Adlib SoftwareHelping enterprises overcome unstructured content challenges, Adlib’s File Analytics solutions allow more than 5,500 customers globally to elevate their content and derive the insight that is needed to support critical decision making, drive efficient business processes, and secure competitive advantage.
  • AdobeLeading the charge to reimagine customer experience management (CXM) with Adobe Experience Cloud, a solution for marketing, advertising, analytics, and commerce, Adobe in 2018 acquired Marketo, a provider of software for B2B marketing engagement.
  • AINSA provider of cloud-based, adaptive case management platforms and solutions for government and commercial markets, AINS helps customers bring new products to market quickly, digitize customer engagement, and automate business processes without the constraints of custom coding.
  • AlgoliaWith its mission to make every search interaction rewarding through its developer-friendly and enterprise-grade APIs, Algolia provides a hosted platform that reduces the complexity of building and scaling a fast, relevant digital experience and helps teams accelerate development time.
  • Amazon Web Servicesroviding a secure cloud services platform that offers compute power, database storage, content delivery, and other functionality to help businesses scale and grow, AWS enables customers to build sophisticated applications with increased flexibility, scalability, and reliability.
  • Ampliance global leader in cloud-based content management and asset management software-as-a-service, Amplience was founded in 2008 to simplify how clients plan, create, manage, and deliver content and serves more than 200 of the world’s leading retailers.
  • AppianOffering a software development platform that combines intelligent automation and enterprise low-code development, Appian applications are used by many of the world’s largest organizations to improve customer experience, achieve operational excellence, and simplify global risk and compliance.
  • AsanaFrom company objectives to digital transformation to product launches and marketing campaigns, Asana helps teams organize and manage their work, and counts companies such as Airbnb, Disney, KLM Air France, NASA, Overstock.com, Uber, and Zalando among its customers.
  • ASG TechnologiesA provider of solutions for information access, management, and control, ASG helps companies to find, understand, govern, and deliver information of any kind, from any source—whether structured or unstructured—through its lifecycle, from capture to analysis to consumption.
  • Atlassian Confluenceroviding collaboration software developed and published by Australian software company Atlassian, Confluence is offered on-prem or SaaS, with nearly 3,000 additional Atlassian apps for Confluence also available in the Atlassian Marketplace to custom-fit Confluence to a team’s needs.
  • AttivioDelivering intelligent answers and insights that enable companies to address the most complex questions asked by their employees, customers, and support teams, the Attivio platform features capabilities such as data aggregation, natural language processing, machine learning, analytics, and knowledge graphing.
  • AuraPortalProviding solutions for enterprises and organizations of any size—from small businesses, right through to large corporations—AuraPortal is a BPM software company that enables easy design and execution of operating processes without additional programming requirements.
  • BA InsightAn innovator in AI-driven search, BA Insight is helping to power a new generation of intranets and AI-driven search solutions by connecting machine learning, cognitive computing, and enterprise systems to provide personalized and relevant results. To Learn more, read Masood Zarrabian, CEO's View from the Top.
  • BloomReachWith applications for content management, site search, page management, SEO optimization, and role-based analytics, BloomReach offers an open and intelligent digital experience platform to help accelerate the path to conversion, increase revenue, and generate customer loyalty.
  • BoxImproving how organizations work by securely connecting their people, information, and applications, Box offers a cloud content management and file-sharing service to enable all team members to access, edit, share, and comment on all work files from any device.
  • BP LogixA provider of low-code/no-code BPM solutions for rapid digital application development, BP Logix helps IT and business users to deploy digital, workflow-driven solutions that cross organizational boundaries with less time and cost than that of traditional development.
  • Cambridge SemanticsOffering a universal semantic layer to connect and bring meaning to all enterprise data, Cambridge Semantics enables IT departments and their business users to semantically link, analyze, and manage diverse data, whether internal or external, structured or unstructured.
  • CGIFounded in 1976, CGI is an independent IT and business consulting services firm that delivers an end-to-end portfolio of capabilities, from IT and business consulting to systems integration, outsourcing services, and intellectual property solutions, to help clients digitally transform their organizations and accelerate results.
  • CitrixSupporting environments where people, organizations, and things are securely connected and accessible, Citrix seeks to help customers reimagine the future of work by providing secure digital workspaces that unify the apps, data, and services people need to be productive.
  • ClarabridgeOffering a comprehensive solution for omni-source listening, customer and text analytics, and real-time, guided action, Clarabridge helps organizations power their CX programs and drive a customer-focused strategy.
  • CogniVisionHelping improve knowledge worker productivity, CogniVision provides an enterprise productivity platform for electronic content management, including a search engine that helps users find information faster and a workspace console tightly integrated with MS Office products and key functionalities.
  • ConfirmitA SaaS vendor for multi-channel customer experience, employee engagement, and market research solutions, Confirmit powers companies worldwide with a range of products for feedback/data collection, panel management, data processing, analysis, and reporting.
  • Connotate (acquired by Import.io)A provider of web content extraction software, Connotate uses a combination of proven technology and real-world experience to turn the web’s big data into a worldwide database for information service providers and other data-centric companies.
  • CrownpeakOffering an enterprise-grade, SaaS digital experience management platform that features content management, personalization, search, and hosting, Crownpeak helps Fortune 2000 companies to create, deploy, and optimize customer experiences across global digital touchpoints at scale.
  • DeloitteAs a provider of audit and assurance, consulting, financial ad­visory, risk advisory, tax, and related services, Deloitte is relied on by clients to help them transform uncertainty into possibility and rapid change into lasting progress.
  • DocuwareDelivering document management and workflow automation software that enables organizations to digitize and optimize the processes that power their businesses, Docuware helps remove manual tasks so knowledge workers are free to focus on projects that drive productivity.
  • DropboxSeeking to transform the way people work together, Dropbox offers a global collaboration platform for content creation, access, and sharing and has more than 500 million registered users across 180 countries.
  • Earley Information ScienceAs a specialized information agency, Earley uses methodologies designed specifically to address product data, content assets, customer data, and corporate knowledge bases and supports business outcomes by organizing data to make it findable, usable, and valuable.
  • eGainPowering digital trans-formation for leading brands, eGain provides cloud-based customer engagement solutions for social, mobile, web, and contact centers that help clients deliver connected and personalized customer journeys in an omnichannel world.    
    EgnyteWith a mission to help organizations to protect, connect, and unlock value from all their content, Egnyte enables secure content collaboration, compliant data protection, and simple infrastructure modernization through a single SaaS solution.
  • EmpolisHelping to turn data into insights, and insights into decisions, Empolis provides smart information management software for comprehensive creation, management, analysis, intelligent processing, and provisioning of information, available as SaaS and on-premise. To Learn more, read Dr. Stefan Wess, CEO's View From the Top.
  • Enterprise KnowledgeA services firm that integrates knowledge management, information management, information technology, and agile approaches, Enterprise Knowledge’s mission is to partner with clients to enable tailored and results-oriented solutions that enable them to thrive and adapt to change. To Learn more, read Zach Wahl, CEO's View From the Top.
  • EPAMA global provider of software engineering and IT consulting services, EPAM’s multi-disciplinary teams help customers digitally transform their businesses with a combination of business design thinking, engineering, modern operations practices, ?and a knowledge of leading tools and frameworks.
  • EphesoftTurning unstructured content into actionable data, Ephesoft provides supervised learning-powered document and data capture solutions that help customers put information to work and do business faster while reducing errors, improving workflow, and saving money.
  • EpiserverA global software company, Episerver provides its digital experience cloud to unify digital content, commerce, and marketing in one platform, including omnichannel solutions for smart personalization and intelligent campaigns.
  • EQT-SitecoreA provider of customer experience management, digital marketing, and ecommerce software, Sitecore has served more than 5,200 brands—enabling them to drive business performance through tailored content across channels, increasingly facilitated by AI and machine learning.
  • e-SpiritHelping companies to unlock the value of digital content, e-Spirit provides a content management system that enables them to connect people, systems, and applications to the centralized, real-time data, assets, and information necessary to publish content across channels and devices.
  • EverteamIn an increasingly complex legal and compliance environment, Everteam brings 25 years of experience and innovation to the field of enterprise content management, enabling enterprises to build and manage content-driven processes that support a range of business opportunities.
  • Expert SystemUsing intelligent technology and applications that provide an accurate, automatic, and immediate understanding of text, Expert System helps organizations accelerate business processes, improve information management, and make smarter decisions across media, customer care, compliance, third-party risk mitigation, and intelligence applications.
  • The Firm Directory (Unika)Unika, the next-generation of Neudesic’s Firm Directory, has enhanced features to help law firms and legal departments optimize the expertise of their lawyers and other personnel, and offers greater capabilities to help firms create, find, and share knowledge. To Learn more, read Jason  Noble, President, Digital Innovation Group, Unika.ai's View from the Top.
  • Fujitsu Computer Products of AmericaA subsidiary of the Japanese information and communication technology company Fujitsu Ltd., Fujitsu Computer Products of America is a leader in document imaging, offering scanning solutions and services that help customers to solve critical business productivity issues and streamline operations.
  • FunnelbackBelieving that search underpins great solutions, Funnelback provides website and enterprise search and strives to produce solutions that tackle a wide range of problems, from course finders for higher education to predictive segmentation and dynamic curation tools for marketing teams.
  • GoodDataOn a mission to fundamentally change the way that businesses make decisions, GoodData offers an analytics platform-as-a-service technology, and expertise to operationalize intelligent decisions within business applications and business processes.
  • GoogleLong-known for its ubiquitous search technology, Google continues to expand its range of products and services with additional search tools, advertising services, communication and publishing tools, development tools, security tools, map-related products, statistical tools, and desktop apps.
  • harmon.ieharmon.ie provides user experience tools for the digital workplace to enable a cohesive, people-first user experience supported by cognitive science and powered by machine learning that enhances employee productivity and helps organizations find relevant insights and information.
  • HPE (Hewlett Packard Enterprise)—A multinational enterprise information technology company, HPE is focused on developing intelligent solutions that allow customers to capture, analyze, and act upon data seamlessly, from edge to core to cloud, so they can improve business outcomes.
  • Hyland, creator of OnBaseWith a single low-code enterprise information platform for enterprise content management, case management, business process management, records management, and capture, OnBase by Hyland helps organizations to digitize their workplaces and fundamentally transform their operations for greater agility, efficiency, and effectiveness.
 
  • IBMWith products and services spanning analytics, blockchain, AI, collaboration, and customer engagement, Big Blue continues to push boundaries, recently buying Red Hat in the biggest acquisition in its history and unveiling Project Debater to facilitate intelligent debate.
  • Igloo SoftwareA provider of cloud-based, mobile-enabled digital workplace solutions that intgrate with leading enterprise systems and cloud apps, Igloo helps companies move beyond traditional intranets to inspiring digital destinations that improve communication, knowledge sharing, collaboration, and culture.
  • iManageAiming to transform how professionals in legal, accounting, and financial services get work done, iManage combines AI, security, and risk mitigation with document and email management to automate routine cognitive tasks, provide insights, and streamline work.
  • InbentaInbenta specializes in natural language processing and semantic search to improve the customer experience by providing support services such as dynamic FAQs, knowledge management, and chatbots that improve business website searches, customer self-service, and ecommerce conversions.
  • IngeniuxA provider of intelligent content management and digital experience software, Ingeniux empowers organizations with a .NET platform for websites, portals, communities, and structured content delivery, available as a hosted service (SaaS) or an on-premise application.
  • Interfacing TechnologiesInterfacing Technologies provides business process management software tools that allow business users to model, map, and manage business processes and knowledge with solutions that are designed for business users, facilitating multiple organizational programs within a single platform.
  • KenticoKentico EMS allows users to manage contacts and campaigns, track customer journeys, provide global ecommerce functionality, and measure and analyze the results to create and refine customer experiences in a dynamic business environment.
  • KnosysKnosys specializes in knowledge management, offering the platform KnowledgeIQ, which unlocks the intelligence within an organization to help employees learn faster and work smarter with information available throughout the organization.
  • LaserficheWith intuitive on-premise and cloud solutions for document management and process automation, Laserfiche improves productivity, efficiency, and strategic decision making for organizations looking to transform into a digital workplace for the future.
  • Litera MicrosystemsLitera Microsystems provides software for drafting, proofreading, comparing, repairing, cleaning, and securing documents in the legal and life sciences industries worldwide with core products that empower users to generate, review, and distribute high-quality content quickly and securely, from any device.
  • LucidworksLucidworks builds AI-powered search and discovery applications such as Fusion, its advanced development platform, which provides the enterprise-grade capabilities needed to design, develop, and deploy intelligent search apps at any scale.
  • MarkLogicMarkLogic offers an operational and transactional enterprise NoSQL database platform that integrates an organization’s critical data and builds innovative applications on a 360-degree view of that data so companies can have an interacting overview of their data.
  • M-FilesUsing AI technologies in its unique Intelligent Metadata Layer, M-Files breaks down silos by delivering an in-context experience for accessing and leveraging information that resides in any system and repository, including network folders, SharePoint, file sharing services, ECM systems, CRM, and more.
  • MicrosoftMicrosoft develops, licenses, and supports a range of software products and services in such areas as productivity, business processes, and intelligent cloud for any and every kind of knowledge management process.
  • MindbreezeMindbreeze provides appliances and cloud services for enterprise search, applied AI, and knowledge management so companies can generate a knowledge database that allows businesses to find data more quickly.To Learn more, read Daniel Fallman, Founder & CEO's View From the Top.
  • NetDocumentsNetDocuments is the trusted, cloud-based content services and workflow platform for lawyers and knowledge workers, complete with built-in security, compliance, information governance, disaster recovery, matter centricity, enterprise search, mobility, records management, and collaboration.To Learn more, read Alvin Tedjamulia, CTO's View from the Top.
  • NintexNintex offers process management and automation through the Nintex Platform to accelerate progress on digital transformation journeys by quickly and easily managing, automating, and optimizing business processes throughout the enterprise.
  • NuxeoNuxeo, developer of a content services platform, changes how people work with data and content to realize new value from digital information with its cloud-native platform that has been deployed by large enterprises, mid-sized businesses, and government agencies worldwide.
  • OneSpanEncompassing digital onboarding, fraud mitigation, workflow management, and e-signature capabilities, OneSpan’s unified, open platform helps reduce costs, accelerate customer acquisition, and increase customer satisfaction so that even companies in the most regulated industries can embrace digital transformation.
  • OnixOnix creates targeted solutions with infrastructure, collaboration, devices, enterprise search, and location-based services using products from such industry leaders as Google Cloud and Amazon Web Services (AWS), among others in the field.
  • OpenTextOpenText enables organizations to gain insight through enterprise information management solutions, on-premises or in the cloud, enabling businesses to grow faster, lower operational costs, and reduce information governance and security risks.
  • OracleOracle offers complete SaaS application suites for ERP, HCM and CX, plus best-in-class database platform as a service (PaaS) and infrastructure as a service (IaaS) for any type of knowledge management workload.
  • Panasonic System Communications Company of North America—From conversion to digitizing and smart routing of files, Panasonic embraces disruptive innovation to create knowledge management solutions by offering a plethora of document scanning technologies, security, and support for all types of businesses.
  • PaperSavePaperSave simplifies the document capture process through automation, streamlines processes by removing redundancies, and provides sophisticated search and retrieval features for better records management to uncover insights faster and more efficiently.
  • ParascriptParascript provides smart self-learning document processing automation for any document with any data from any source with its image-based analysis, classification, data location, extraction, and verification technology for clear insights.
  • PingarPingar is a platform-agnostic software company that offers classification and metadata generation that enables enterprises to identify and extract information from unstructured data faster, securely, and more efficiently than before.
  • QlikCompanies use Qlik’s analytics and data management platform to see more deeply into customer behavior, reinvent business processes, discover new revenue streams, and balance risk and reward when developing new innovations.
  • QuarkQuark develops content automation and sales enablement solutions that help mid-to-large organizations streamline the creation, management, publishing, and delivery of business-critical information throughout the enterprise.
  • QuestA global systems management, data protection, and security software provider, Quest Software delivers solutions for the rapidly-changing world of enterprise IT to help address the challenges caused by data growth, cloud expansion, hybrid data centers, security threats, and regulatory requirements.
  • RelativityRelativity’s e-discovery platform is used by thousands of organizations around the world to manage large volumes of data and quickly identify key issues during litigation, internal investigations, and compliance projects.
  • ReltioReltio provides Reltio Cloud, a modern master data management platform for a 360-degree customer view used by Global 2000 companies to power their digital transformation and data compliance initiatives starting from Day 1.
  • Rivet LogicHelping organizations build riveting digital experiences, and the solutions to manage and optimize them, Rivet Logic is an award-winning consulting, design, and systems integration firm that leverages leading open source and cloud software.
  • SASSAS develops analytics, business intelligence, and data management software to transform data into intelligence that helps customers make better decisions and reach conclusions faster, smarter, and more efficiently.
  • SearchBloxSearchBlox provides enterprise search, sentiment analysis, and text analytics solutions based on Elasticsearch and Apache Lucene to address customers’ data management needs, including web-based administration and integrated data connectors to index enterprise and web content.
  • SeismicSeismic provides sales and marketing enablement, equipping global sales teams with the knowledge, messaging, and automatically personalized content proven to be the most effective for any buyer interaction anytime, anywhere.
  • Semantic Web CompanyA provider of graph-based metadata, search, and analytic solutions, Semantic Web Company helps companies manage corporate knowledge graphs, extract useful knowledge from big data sets, and integrate both structured and unstructured data.
  • SinequaSinequa provides a cognitive search and analytics platform for Global 2000 companies and government agencies that connects people with the information, expertise, and insights necessary for organizations to become information-driven.
  • Smartlogic SemaphoreSmartlogic’s Semaphore is an enterprise-grade semantic platform that allows organizations to realize the business value of their information by using a model-driven, rule-based semantic approach that solves complex business problems that traditional technologies cannot.
  • SmartSheetSmartsheet provides a cloud-based platform for execution of work, enabling teams and organizations to plan, capture, manage, automate, and report on work so businesses can plan more ideas, gain more customers, and add more revenue
  • Summit 7 SystemsSummit 7 is helping organizations find “a new way to work” with cutting-edge cloud technologies through thoughtful leadership, technical expertise, and compliant platforms that work with Microsoft’s suite of products.
  • SynapticaSynaptica provides a text analytics platform that enables enterprises to analyze content collections, extract named entities, and perform precision inline concept indexing and categorization so taxonomy management is simple and efficient.
  • Syncplicity by AxwaySyncplicity by Axway is a hybrid enterprise file sync and share and mobile collaboration solution that provides users with the experience and tools they desire and gives IT the security and control it needs.
  • SystemwareSystemware cultivates digital ecosystems with its content network, Content Cloud, a hybrid offering designed for enterprises and its people that connects, manages, and enables billions of documents to obtain insights.
  • TableauTableau’s mission is to help people see and understand data through solutions that quickly analyze, visualize, and share information so customer accounts get rapid results with Tableau in the office and on-the-go.
  • TIBCOFrom APIs and systems to devices and people, TIBCO interconnects everything, captures data in real time wherever it is, and augments the intelligence of businesses through analytical insights so companies can make faster decisions.
  • TransversalTransversal provides cognitive knowledge automation solutions for the cloud that make self-service more intuitive, front-office applications more intelligent, and knowledge management easier for customers, agents, employees, and the future.
  • Upland Software—RightAnswersGetting the right answers to the right people at the right time, Upland’s RightAnswers software enables IT help desk and customer service agents to be more productive with a centralized knowledge hub to create, maintain, and locate relevant information quickly. To Learn more, read Laura Lockley, Head of Customer Success' View From the Top.
  • Vanguard SoftwareRelied on by companies across every major industry, Vanguard Software provides collaborative, web-based planning and advanced analytic solutions that unite roles, teams, and departments to help them achieve accurate forecasts.
  • Veeva (Vault Platform)—Architected to meet rigorous usability, scalability, performance, validation, and security requirements, Veeva’s Vault Platform for cloud application development platforms enables organizations to customize integrate, and extend Vault applications, or create their own applications.
  • VerintOffering actionable intelligence solutions with a focus on customer engagement optimization, security intelligence, fraud, risk, and compliance, Verint helps organizations of all sizes and across many industries to make informed, timely, and effective decisions. To Learn more, read Elan Moriah, President, Customer Engagement Solutions' Verint® View From the Top.
  • WrikeWrike provides a SaaS-based collaborative work management platform that helps teams and organizations reach operational excellence with solutions that will give companies clarity and accountability, real-time visibility, and accelerate results.
  • XeroxFocusing on the intersection of people, paper, and processes, Xerox DocuShare offers enterprise content management designed with usability, flexibility, and convenience in mind, helping knowledge workers be more efficient every day.
  • Zoho CorporationWith apps in nearly every major business category, including sales, marketing, customer support, accounting, and back office operations, and an array of productivity and collaboration tools, Zoho is the “operating system for business.”

Source: KM World

In Collaboration with Huntertech Global

Scientists say they have developed a novel machine learning model to classify different types of lung cancer, and found that it performed on par with three practicing pathologists.

WASHINGTON: Scientists say they have developed a novel machine learning model to classify different types of lung cancer, and found that it performed on par with three practicing pathologists.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Machine learning has improved dramatically in recent years and shown great promise in the field of medical image analysis, said researchers from Dartmouth-Hitchcock Medical Centre (DHMC) in the US.

They utilised machine learning capabilities to assist with the challenging task of grading tumour patterns and subtypes of lung adenocarcinoma, the most common form of the leading cause of cancer-related deaths worldwide.

Currently, lung adenocarcinoma, requires pathologist's visual examination of lobectomy slides to determine the tumour patterns and subtypes, according to the study published in the journal Scientific Reports.

A lobectomy is a type of lung cancer surgery in which one lobe of a lung is removed.

This classification has an important role in prognosis and determination of treatment for lung cancer, however is a difficult and subjective task.

"Our study demonstrates that machine learning can achieve high performance on a challenging image classification task and has the potential to be an asset to lung cancer management," said Saeed Hassanpour, who led the study.

"Clinical implementation of our system would be able to assist pathologists for accurate classification of lung cancer subtypes, which is critical for prognosis and treatment," he said in a statement.

Using recent advances in machine learning, the team developed a deep neural network to classify different types of lung adenocarcinoma on histopathology slides, and found that the model performed on par with three practicing pathologists.

Recognising that the approach is potentially applicable to other histopathology image analysis tasks, the researchers made their code publicly available to promote new research and collaborations in this domain.

In addition to testing the deep learning model in a clinical setting to validate its ability to improve lung cancer classification, the team plans to apply the method to other challenging histopathology image analysis tasks in breast, esophageal, and colorectal cancer.

"If validated through clinical trials, our neural network model can potentially be implemented in clinical practice to assist pathologists," said Hassanpour.

"Our machine learning method is also fast and can process a slide in less than one minute, so it could help triage patients before examination by physicians and potentially greatly assist pathologists in the visual examination of slides," he said. 

Source: ET Health world

With supercomputers, scientists find promising new materials for solar cells.

Finding the best light-harvesting chemicals for use in solar cells can feel like searching for a needle in a haystack. Over the years, researchers have developed and tested thousands of different dyes and pigments to see how they absorb sunlight and convert it to electricity. Sorting through all of them requires an innovative approach.

Now, thanks to a study that combines the power of supercomputing with data science and experimental methods, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Cambridge in England have developed a novel ​design to device” approach to identify promising materials for dye-sensitized solar cells (DSSCs). DSSCs can be manufactured with low-cost, scalable techniques, allowing them to reach competitive performance-to-price ratios.

The team, led by Argonne materials scientist Jacqueline Cole, who is also head of the Molecular Engineering group at the University of Cambridge’s Cavendish Laboratory, used the Theta supercomputer at the Argonne Leadership Computing Facility (ALCF) to pinpoint five high-performing, low-cost dye materials from a pool of nearly 10,000 candidates for fabrication and device testing. The ALCF is a DOE Office of Science User Facility.

This was a particularly encouraging result because we had made our lives harder by restricting ourselves to organic molecules for environmental reasons, and yet we found that these organic dyes performed as well as some of the best known organometallics.” — Jacqueline Cole

This study is particularly exciting because we were able to demonstrate the full cycle of data-driven materials discovery — from using advanced computing methods to identify materials with optimal properties to synthesizing those materials in a laboratory and testing them in actual photovoltaic devices,” Cole said.

Through an ALCF Data Science Program project, Cole worked with Argonne computational scientists to create an automated workflow that employed a combination of simulation, data mining and machine learning techniques to enable the analysis of thousands of chemical compounds concurrently. The process began with an effort to sort through hundreds of thousands of scientific journals to collect chemical and absorption data for a wide variety of organic dye candidates.

The advantage of this process is that it takes away the old manual curation of databases, which involves many years’ worth of work, and reduces it to a matter of a few months and, ultimately, a few days,” Cole said.

The computational work involved using finer and finer screening techniques to generate pairs of potential dyes that could work in combination with each other to absorb light across the solar spectrum. ​It’s almost impossible to find one dye that really works well for all wavelengths,” Cole said. ​This is particularly true with organic molecules because they have narrower optical absorption bands; and yet, we really wanted to concentrate just on organic molecules, because they are significantly more environmentally friendly.”

To narrow the initial batch of 10,000 potential dye candidates down to just a few of the most promising possibilities involved again using ALCF computing resources to carry out a multistep approach. First, Cole and her colleagues used data mining tools to eliminate any organometallic molecules, which generally absorb less light than organic dyes at a given wavelength, and organic molecules that are too small to absorb visible light.

Even after this first pass, the researchers still had approximately 3,000 dye candidates to consider. To further refine the selection, the scientists screened for dyes that contained carboxylic acid components that could be used as chemical ​glues,” or anchors, to attach the dyes to titanium dioxide supports. Then, the researchers used Theta to conduct electronic structure calculations on the remaining candidates to determine the molecular dipole moment — or degree of polarity — of each individual dye.

We really want these molecules to be sufficiently polar so that their electronic charge is high across the molecule,” Cole said. ​This allows the light-excited electron to traverse the length of the dye, go through the chemical glue, and into the titanium dioxide semiconductor to start the electric circuit.”

After having thus narrowed the search to approximately 300 dyes, the researchers used their computational setup to examine their optical absorption spectra to generate a batch of roughly 30dyes that would be candidates for experimental verification. Before actually synthesizing the dyes, however, Cole and her colleagues performed computationally intensive density functional theory (DFT) calculations on Theta to assess how each of them were likely to perform in an experimental setting.

The final stage of the study involved experimentally validating a collection of the five most promising dye candidates from these predictions, which required a worldwide collaboration. As each of the different dyes had been initially synthesized in different laboratories throughout the world for some other purpose, Cole reached out to the original dye developers, each of whom sent back a new sample dye for her team to investigate.

It was really a tremendous bit of teamwork to get so many people from around the world to contribute to this research,” Cole said.

In looking at the dyes experimentally at Argonne’s Center for Nanoscale Materials, another DOEOffice of Science User Facility, and at the University of Cambridge and the Rutherford Appleton Laboratory, Cole and her colleagues discovered that some of them, once embedded into a photovoltaic device, achieved power conversion efficiencies roughly equal to that of the industrial standard organometallic dye.

This was a particularly encouraging result because we had made our lives harder by restricting ourselves to organic molecules for environmental reasons, and yet we found that these organic dyes performed as well as some of the best known organometallics,” Cole said.

A paper based on the study, ​Design-to-device approach affords panchromatic co-sensitized solar cells,” appeared as the cover article in the February 1 issue of Advanced Energy Materials. Other Argonne authors of the paper included Liliana Stan and Álvaro Vázquez-Mayagoitia. Authors from the University of Cambridge, Rutherford Appleton Laboratory (UK), Indian Institute of Technology-Roorkee, Tianjin University of Technology (China), Hong Kong Baptist University, University of Zaragoza (Spain), and University of Naples (Italy) also contributed.

The research was funded by the DOE’s Office of Science.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit the Office of Science website.

Source: Argonne National Laboratory

 

Page 2 of 4

© copyright 2017 www.aimlmarketplace.com. All Rights Reserved.

A Product of HunterTech Ventures