SAN DIEGO, Sept. 28, 2021 /PRNewswire/ --TheAssociation for Computing Machinery(ACM) Special Interest Group onKnowledge Discovery and Data Mining (SIGKDD) today announced the recipients of the SIGKDD Best Paper Awards, recognizing papers presented at the annual SIGKDD conference that advance the fundamental understanding of the field of knowledge discovery in data and data mining. Winners were selected from more than 2,200 papers initially submitted for consideration to be presented at KDD 2021, which took place Aug. 14-18. Of the 394 papers chosen for the conference, three awards were granted: Best Paper in the Research Track, Best Paper in the Applied Data Science Track, and Best Student Paper.

"Academic and industrial researchers from all over the world submitted papers to KDD 2021 to showcase the newest innovations in the field of machine learning knowledge discovery," noted Dr. Haixun Wang, chair of the SIGKDD award committee. "Those selected for recognition have pushed the frontier of machine learning especially in tackling real-world problems." The SIGKDD Best Papers of 2021 are as follows:

The technical program committees for the Research Track and the Applied Data Science Track identified and nominated a highly selective group of papers for the Best Paper Awards. The nominated papers were then independently reviewed by a committee led by Chair Haixun Wang, vice president of engineering and algorithms at Instacart; Professor Wei Wang, University of California, Los Angeles; Professor Beng Chin, National University of Singapore; Professor Jiawei Han, University of Illinois at Urbana-Champaign; and Sanjay Chawla, research director of Qatar Computing Research Institute's data analytics department.

For more information on KDD 2021, please visit: https://www.kdd.org/kdd2021/.

About ACM SIGKDD: ACM is the premier global professional organization for researchers and professionals dedicated to the advancement of the science and practice of knowledge discovery and data mining.SIGKDD is ACM's Special Interest Group on Knowledge Discovery and Data Mining.The annual KDD International Conference on Knowledge Discovery and Data Miningis thepremierinterdisciplinary conference for data mining, data science and analytics.

Follow KDD:Facebook https://www.facebook.com/SIGKDD Twitter https://twitter.com/kdd_news LinkedInhttps://www.linkedin.com/groups/160888/

SOURCE ACM SIGKDD

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KDD 2021 Honors Recipients of the SIGKDD Best Paper Awards - PRNewswire

Palantir (NYSE:PLTR) and IBM (NYSE:IBM) are two very different types of tech companies. Palantir's market value has tripled since its direct listing last September, thanks to the robust growth of its data mining and AI platforms. IBM, which went public 110 years ago, has lost about a fifth of its value over the past decade as it struggled to grow its legacy businesses.

Palantir is now worth $56 billion, while IBM is worth $123 billion. But could Palantir's market value soar and eclipse Big Blue's by 2025? Let's dive deeper into both companies' plans for the future to find out.

Image source: Getty images.

Palantir's revenue rose 47% to $1.1 billion in 2020, and it expects its revenue to rise more than 30% annually from 2021 to 2025 -- which implies it will generate at least $4 billion in revenue in 2025.

Palantir's stock currently trades at 37 times this year's sales. If it maintains that premium price-to-sales ratio, it could be worth $148 billion by the beginning of 2025, and be more valuable than today's IBM.

Palantir expects that growth to be driven by the expansion of its two core platforms: Gotham, which serves government clients; and Foundry, which provides lighter versions of those services for enterprise clients. Its third platform, Apollo, provides cloud-based updates to both platforms.

Palantir expects Gotham, which accumulates and analyzes intel from a wide range of disparate sources, to become the "default operating system for data across the U.S. government." Gotham already serves all branches of the U.S. military, the FBI, CIA, ICE, and other agencies, and it will likely gain even more contracts as the government upgrades its technological infrastructure.

Palantir's hardened reputation could also convince more enterprise customers to use Foundry to analyze their data and optimize their businesses.

IBM's annual revenue declined from $99.9 billion in 2010 to $73.6 billion in 2020. Throughout that lost decade, IBM divested its weaker businesses and attempted to expand its cloud-oriented businesses.

Unfortunately, IBM couldn't offset the slower growth of its legacy hardware, software, and IT services businesses with the expansion of those newer cloud businesses. It also struggled to keep pace with Amazon, Microsoft, and Alphabet's Google in the public cloud market.

IBM's turnaround strategy, which is being led by a new CEO who took the helm last April, is to divest its slower-growth managed infrastructure services segment into a new company called Kyndryl by the end of 2021. It then plans to improve the "new" IBM's hybrid cloud and AI businesses, which were accelerated by its acquisition of Red Hat two years ago, to generate fresh sales growth.

After it completes Kyndryl's spin-off, IBM expects to grow its revenue by the mid-single-digits in 2022 and beyond. However, Kyndryl's businesses generated more than a quarter of IBM's total revenue last year, so the "new" IBM could be valued at roughly three-quarters of the "old" IBM.

If the "new" IBM grows its revenue 5% annually through 2025, it could generate about $70 billion in annual revenue by the final year. IBM currently trades at just 1.6 times this year's sales. But if IBM's newfound growth convinces investors to pay a slightly higher price-to-sales ratio of 2.0, the "new" IBM might be worth about $140 billion by 2025.

If Palantir achieves its ambitious growth targets, its stock could certainly be worth more than the "new" IBM by 2025. However, Palantir will still likely be worth less than the combined value of IBM and Kyndryl, which might grow faster as a stand-alone IT services company that isn't burdened with supporting IBM's higher-growth hybrid cloud and AI businesses.

Palantir and IBM should still appeal to different types of investors over the next four years, but I believe the former will remain a stronger investment than the latter. Palantir's stock is pricier, but its core businesses will likely keep expanding as IBM tries to streamline its sprawling business.

This article represents the opinion of the writer, who may disagree with the official recommendation position of a Motley Fool premium advisory service. Were motley! Questioning an investing thesis -- even one of our own -- helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.

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Will Palantir Be Worth More Than IBM by 2025? - Motley Fool

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Open source technology has been a driving factor in many of the most innovative developments of the digital age, so it should come as no surprise that it has made its way into artificial intelligence as well.

But with trust in AIs impact on the world still uncertain, the idea that open source tools, libraries, and communities are creating AI projects in the usual wild west fashion is creating yet more unease among some observers.

Open source supporters, of course, reject these fears, arguing that there is just as little oversight into the corporate-dominated activities of closed platforms. In fact, open source can be more readily tracked and monitored because it is, well, open for all to see. And this leaves us with the same question that has bedeviled technology advances through the ages: Is it better to let these powerful tools grow and evolve as they will, or should we try to control them? And if so, how and to what extent?

If anything, says Analytics Insights Adilin Beatrice, open source has fueled the advance of AI by streamlining the development process. There is no shortage of free, open source platforms capable of implementing even complex types of AI like machine learning, and this serves to expand the scope of AI development in general and allow developers to make maximum use of available data. Tools like Weka, for instance, allow coders to quickly integrate data mining and other functions into their projects without having to write it all from scratch. Googles TensorFlow, meanwhile, is one of the most popular end-to-end machine learning platforms on the market.

And just as weve seen in other digital initiatives, like virtualization and the cloud, companies are starting to mix-and-match various open source solutions to create a broad range of intelligent applications. Neuron7.ai recently unveiled a new field service system capable of providing everything from self-help portals to traffic optimization tools. The system leverages multiple open AI engines, including TensorFlow, that allow it to not only ingest vast amounts of unstructured data from multiple sources, such as CRM and messaging systems, but also encapsulate the experiences of field techs and customers to improve accuracy and identify additional means of automation.

One would think that with open source technology playing such a significant role in the development of AI that it would be at the top of the agenda for policy-makers. But according to Alex Engler of the Brookings Institution, it is virtually off the radar. While the U.S. government has addressed open source with measures like the Federal Source Code Policy, more recent discussions on possible AI regulations mention it only in passing. In Europe, Engler says open source regulations are devoid of any clear link to AI policies and strategies, and the most recently proposed updates to these measures do not mention open source at all.

Engler adds that this lack of attention could produce two negative outcomes. First, it could result in AI initiatives failing to capitalize on the strengths that open source software brings to development. These include key capabilities like increasing the speed of development itself and reducing bias and other unwanted outcomes. Secondly, there is the potential that dominance in open source solutions could lead to dominance in AI. Open source tends to create default standards in the tech industry, and while top open source releases from Google, Facebook, and others are freely available, the vast majority of projects they support are created from within the company that developed the framework, giving them an advantage in the resulting program.

This, of course, leads us back to the same dilemma that has plagued emerging technologies from the beginning, says the IEEEs Ned Potter. Who should draw the roadmap for AI to ensure it has a positive impact on society? Tech companies? The government? Academia? Or should it simply be democratized and let the market sort it out? Open source supporters tend to favor a free hand, of course, with the idea that continual scrutiny by the community will organically push bad ideas to the bottom and elevate good ideas to the top. But this still does not guarantee a positive outcome, particularly as AI becomes accessible to the broader public.

In the end, of course, there are no guarantees. If weve learned anything from the past, mistakes are just as likely to come from private industry as from government regulators or individual operators. But there is a big difference between watching and regulating. At the very least, there should be mechanisms in place to track how open source technologies are influencing AI development so at least someone has the ability to give a heads up if things are heading in a wrong direction.

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Assessing the intersection of open source and AI - VentureBeat

Learn about data mining, which combines statistics and artificial intelligence to analyze large data sets to discover useful information.

Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets. Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge. However, despite the fact that that technology continuously evolves to handle data at a large-scale, leaders still face challenges with scalability and automation.

Data mining has improved organizational decision-making through insightful data analyses. The data mining techniques that underpin these analyses can be divided into two main purposes; they can either describe the target dataset or they can predict outcomes through the use of machine learning algorithms. These methods are used to organize and filter data, surfacing the most interesting information, from fraud detection to user behaviors, bottlenecks, and even security breaches.

When combined with data analytics and visualization tools, like Apache Spark, delving into the world of data mining has never been easier and extracting relevant insights has never been faster. Advances within artificial intelligence only continue to expedite adoption across industries.

The data mining process involves a number of steps from data collection to visualization to extract valuable information from large data sets. As mentioned above, data mining techniques are used to generate descriptions and predictions about a target data set. Data scientists describe data through their observations of patterns, associations, and correlations. They also classify and cluster data through classification and regression methods, and identify outliers for use cases, like spam detection.

Data mining usually consists of four main steps: setting objectives, data gathering and preparation, applying data mining algorithms, and evaluating results.

1. Set the business objectives: This can be the hardest part of the data mining process, and many organizations spend too little time on this important step. Data scientists and business stakeholders need to work together to define the business problem, which helps inform the data questions and parameters for a given project. Analysts may also need to do additional research to understand the business context appropriately.

2. Data preparation: Once the scope of the problem is defined, it is easier for data scientists to identify which set of data will help answer the pertinent questions to the business. Once they collect the relevant data, the data will be cleaned, removing any noise, such as duplicates, missing values, and outliers. Depending on the dataset, an additional step may be taken to reduce the number of dimensions as too many features can slow down any subsequent computation. Data scientists will look to retain the most important predictors to ensure optimal accuracy within any models.

3. Model building and pattern mining: Depending on the type of analysis, data scientists may investigate any interesting data relationships, such as sequential patterns, association rules, or correlations. While high frequency patterns have broader applications, sometimes the deviations in the data can be more interesting, highlighting areas of potential fraud.

Deep learning algorithms may also be applied to classify or cluster a data set depending on the available data. If the input data is labelled (i.e. supervised learning), a classification model may be used to categorize data, or alternatively, a regression may be applied to predict the likelihood of a particular assignment. If the dataset isnt labelled (i.e. unsupervised learning), the individual data points in the training set are compared with one another to discover underlying similarities, clustering them based on those characteristics.

4. Evaluation of results and implementation of knowledge: Once the data is aggregated, the results need to be evaluated and interpreted. When finalizing results, they should be valid, novel, useful, and understandable. When this criteria is met, organizations can use this knowledge to implement new strategies, achieving their intended objectives.

Data mining works by using various algorithms and techniques to turn large volumes of data into useful information. Here are some of the most common ones:

Association rules: An association rule is a rule-based method for finding relationships between variables in a given dataset. These methods are frequently used for market basket analysis, allowing companies to better understand relationships between different products. Understanding consumption habits of customers enables businesses to develop better cross-selling strategies and recommendation engines.

Neural networks: Primarily leveraged for deep learning algorithms,neural networksprocess training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold), and an output. If that output value exceeds a given threshold, it fires or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent. When the cost function is at or near zero, we can be confident in the models accuracy to yield the correct answer.

Decision tree: This data mining technique uses classification or regression methods to classify or predict potential outcomes based on a set of decisions. As the name suggests, it uses a tree-like visualization to represent the potential outcomes of these decisions.

K- nearest neighbor (KNN): K-nearest neighbor, also known as the KNN algorithm, is a non-parametric algorithm that classifies data points based on their proximity and association to other available data. This algorithm assumes that similar data points can be found near each other. As a result, it seeks to calculate the distance between data points, usually through Euclidean distance, and then it assigns a category based on the most frequent category or average.

Data mining techniques are widely adopted among business intelligence and data analytics teams, helping them extract knowledge for their organization and industry. Some data mining use cases include:

Companies collect a massive amount of data about their customers and prospects. By observing consumer demographics and online user behavior, companies can use data to optimize their marketing campaigns, improving segmentation, cross-sell offers, and customer loyalty programs, yielding higher ROI on marketing efforts. Predictive analyses can also help teams to set expectations with their stakeholders, providing yield estimates from any increases or decreases in marketing investment.

Educational institutions have started to collect data to understand their student populations as well as which environments are conducive to success. As courses continue to transfer to online platforms, they can use a variety of dimensions and metrics to observe and evaluate performance, such as keystroke, student profiles, classes, universities, time spent, etc.

Process mining leverages data mining techniques to reduce costs across operational functions, enabling organizations to run more efficiently. This practice has helped to identify costly bottlenecks and improve decision-making among business leaders.

While frequently occurring patterns in data can provide teams with valuable insight, observing data anomalies is also beneficial, assisting companies in detecting fraud. While this is a well-known use case within banking and other financial institutions, SaaS-based companies have also started to adopt these practices to eliminate fake user accounts from their datasets.

Partner with IBM to get started on your latest data mining project. IBM Watson Discovery digs through your data in real-time to reveal hidden patterns, trends and relationships between different pieces of content. Use data mining techniques to gain insights into customer and user behavior, analyze trends in social media and e-commerce, find the root causes of problems and more. There is untapped business value in your hidden insights. Get started with IBM Watson Discovery today.

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What is Data Mining? | IBM

What was supposed to be a celebrated return to a form of normalcy became topsy-turvy as spiking COVID-19 case counts are disrupting a second fall convention season in Anchorage for event planners and those behind the scenes.

Greg Spears, general manager for both of Anchorages city-owned Egan and Denaina convention centers sums up the last year-and-a-half with one word: brutal.

According to Spears, his team has fielded event cancellations totaling roughly $600,000 in just the past month as the resurgent virus, fueled this time by the infamous delta variant, continues to hammer many service-based industries.

Revenue from room rentals this year is trending at about twice the level of last year but is still only about half of what the Egan and Denaina generated in 2019, which Spears referred to as a decent year.

He has taken the approach of doing everything reasonably possible to accommodate everyone who wants to hold in-person gatherings, whether that can still happen now or needs to wait. Once-standard policies of 60 percent deposit refunds for cancellations within 60 days and no refunds inside of 30 days before an event have been relaxed.

It has mostly been around that one-month out timeframe that most event organizers have sought to postpone or outright cancel their plans, Spears said, and staff for the downtown convention centers have done their best to accommodate.

We have been very flexible the last 18-19 months because we understand the situations our clients are in, Spears said in a Sept. 14 interview. Were working with every client so we can hopefully one day get their events back in the building.

While the centers have sizable reserves to draw on from years of profitability, it has not made laying off nearly 100 workers and other unforeseen obstacles any easier to navigate, he added.

The Alaska Oil and Gas Association was among the first local organizations to move its annual conference back in recent weeks. Originally scheduled for Sept. 2 at the Denaina Center, leaders of the industry trade group decided in early August to push the event to mid-January in an attempt to keep attendees as comfortable and safe as possible while also recognizing the deeply rooted desire a growing number of people have to meet again, CEO Kara Moriarty said.

Normally a gathering of about 500 people, the size of the AOGA conference also necessitated considering the potential impact on hospital capacity by holding it as once scheduled, according to Moriarty.

We also consciously made the decision not to go virtual because our audience is ready for an in-person event, she said.

Moriarty also corroborated Spears version of how the cancellations are being handled by convention center officials, saying they were in lock-step with each other on the decision to postpone, again. Once scheduled for May, AOGAs conference had already been moved once this year.

(Denaina Center staff) were really great but we had been in communication with them the whole time, Moriarty said. We moved our date before they had to incur costs they couldnt recover.

She said similar things about the guest speakers and sponsors, nearly all of whom agreed to reschedule or roll deposits to January instead of backing out altogether.

I dont think anyones asked for a refund. At this point, everyone is just, OK, see you in January, Moriarty said.

Alaska Federation of Natives leaders also decided in late August to push their three-day conference one of the largest gatherings annual gatherings of its kind in the state back to mid-December instead of committing to another wholly online event as was done last year. The AFN convention is also held at the Denaina Center when it is in Anchorage.

Visit Anchorage CEO Julie Saupe said Outside groups that hold events in Anchorage are modifying plans as well. Some events first booked for last fall and rebooked for this year are now being pushed to 2023 or 2024 because plans for the interim years have already been made, she said.

More than 1,000 people from Outside were expected to attend the IEEE Signal Processing Society International Conference on Image Processing in Anchorage Sept. 19-22, according to Visit Anchorage spokesman Jack Bonney, who wrote via email that Visit Anchorage officials are working on options with that group and others toward meetings in the city in future years.

Overall, about 70 percent of the events once planned for the Egan and Denaina that have been altered since March 2020 have already been rebooked and more rebookings are expected, according to Bonney.

Anchorage Economic Development Corp. CEO Bill Popp said his group, which holds some of the citys largest luncheons each year, is planning for an in-person 2022 Anchorage Economic Forecast Luncheon in late January after staying virtual for its annual 3-year Economic Outlook presentation held in early August. At the time, COVID-19 cases were just starting to increase significantly across Alaska.

I think people are ready to get back in the three-dimensional world. I think that networking, collegiality are tangible benefits in the minds of most of our constituents, Popp said. The business community, community leadership, the public they all want to be in the room.

Adding to the challenges for Spears have been the widespread labor and supply shortage that has hit the convention centers as well.

Restarting for the Foo Fighters concert here a couple weeks ago was just a major headache in staffing. We enlisted volunteers, took help from family and friends. I myself took on different roles checking vaccination cards and whatnot, Spears said, adding that several members of his core, full-time staff are also currently sidelined with COVID-19 infections of their own.

However, the clear pent-up demand for large gatherings at some point keeps him upbeat about whats to come.

Our future is bright if we can get COVID under control, Spears said.

Elwood Brehmer can be reached at [emailprotected].

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Convention centers stay hopeful in 2nd year of cancellations - Alaskajournal.com