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Monthly Archives: November 2019

Box looks to balance growth and profitability as it matures

November 28, 2019 No Comments

Prevailing wisdom states that as an enterprise SaaS company evolves, there’s a tendency to sacrifice profitability for growth — understandably so, especially in the early days of the company. At some point, however, a company needs to become profitable.

Box has struggled to reach that goal since going public in 2015, but yesterday, it delivered a mostly positive earnings report. Wall Street seemed to approve, with the stock up 6.75% as we published this article.

Box CEO Aaron Levie says the goal moving forward is to find better balance between growth and profitability. In his post-report call with analysts, Levie pointed to some positive numbers.

“As we shared in October [at BoxWorks], we are focused on driving a balance of long-term growth and improved profitability as measured by the combination of revenue growth plus free cash flow margin. On this combined metric, we expect to deliver a significant increase in FY ’21 to at least 25% and eventually reaching at least 35% in FY ’23,” Levie said.

Growing the platform

Part of the maturation and drive to profitability is spurred by the fact that Box now has a more complete product platform. While many struggle to understand the company’s business model, it provides content management in the cloud and modernizing that aspect of enterprise software. As a result, there are few pure-play content management vendors that can do what Box does in a cloud context.

Enterprise – TechCrunch

An introduction to Google Ads Video Ad Sequencing (VAS)

November 27, 2019 No Comments

Video Ad Sequencing (VAS) is a recent addition to the Google Ads video campaign types that allows advertisers to, “…tell your product or brand story by showing people a series of videos in the order that you define.” But it is really a lot more.

Video Ad Sequencing can be used to take your target audience on a video journey based upon, to a limited extent, their behavior. By telling a story VAS lets you drive deeper awareness, engagement, and consideration.

Examples of Video Sequencing usage

Let’s say you want to let people know about “Five key elements of your product” and why it makes you better than the competition. With VAS, you can effectively ensure that potential customers see each video, in a set sequence.

We used VAS with one of our clients which had one long-form video that was just too long to capture the short attention span of users on YouTube. So, instead, we split the ad into five short vignettes, each with a quick intro and value-prop within the first five seconds (which is the non-skippable length of a video ad) to ensure our message got out before a user could skip the full 30-second video. We then set up a VAS campaign that would show these ads, in sequence, so that users would see the full story and all of the value that the product could offer.

What’s great about VAS is that you can go beyond a flat sequence and actually vary the content a user sees, depending on how they interact with each video in the sequence. For example, let’s say a user skips your first ad, rather than having them continue through your sequence, you can say, show them an alternate video outside of your sequence. If they skip that too, then you drop them entirely out of the sequence.

Another potential usage of Video Ad Sequencing

Another potential usage of Video Ad Sequencing is rewarding users for watching your content or calling out when they skip your videos. You can show videos to users that skipped your prior videos in sequence, meaning you can show them alternate content such as alternate value propositions, drop them out of the sequence, or even directly address with the audience that they skipped your prior video but you still really think your product is right for them. Alternatively, if a user views your first video, you can put them into a sequence with longer-form content for the second video, effectively creating exclusive content that only those viewers get to see.

Things you must know

The settings allow for you to dictate what content a user sees after they see an ad (impression) without watching, viewed an ad (watch the full video if shorter than 30-seconds or at least 30-seconds if the video is longer), or skipped an ad.

What you end up with is a flow like this

Video Ad Sequencing example on YouTube


If you are looking to try out video ad sequencing keep this in mind – you are limited to target CPM or Maximum CPV bidding and you cannot target by content.

This means no specific placements, topics, or keywords (you can exclude them though). You can really only target them by demographics and target audiences. YouTube does not currently allow custom affinity or custom intent audiences so you are stuck with life events or In-Market Audiences. Google recommends testing sequencing alongside brand lift studies, which basically means: “This campaign can spend a lot if you let it.”

Available bid strategies

  • Target CPM (Recommended by Google)
    • With Target CPM, we optimize bids to show your entire sequence campaign to your audience, which can help you get a higher sequence completion rate.
  • Maximum CPV

Ad formats include the following

  •  Skippable in-stream ads
  •  Non-skippable in-stream ads
  •  Bumper ads
  •  A combination of the above

The bid strategy you select also dictates the ad formats you can use

Bidding type                                             Available formats

Target CPM (tCPM)                                  Skippable in-stream ads

Non-skippable in-stream ads

Bumper ads

A combination of the above

Maximum CPV (CPV)                              Skippable in-stream ads

Source: Google

I would also strongly recommend mapping out your sequence before-hand. Every step of a sequence is set as a new ad group in the campaign, so it can get big and messy quite quickly.

It’s also good to know how you want to deal with the different interactions at different steps in the sequence. Just because a user skips one video, doesn’t mean they won’t watch another and get back into sequence. But similarly, if a user skips your video(s), do you really want to keep showing them ads in the sequence they care nothing about? Maybe at that point, you show them a totally unrelated tried-and-true video and then drop them out of the sequence.

My testing with Video Ad Sequencing so far has been limited, but I am very excited about the opportunity to keep working with several of our larger clients on sequencing. It is a really powerful tool that Google has shown can grow brand awareness and consideration.

Next, I’ll have a guide for setting up your first video ad sequence should you still need help.

The post An introduction to Google Ads Video Ad Sequencing (VAS) appeared first on Search Engine Watch.

Search Engine Watch

November Updates to Paid Advertising Platforms

November 27, 2019 No Comments

In this monthly post, we bring you the latest from all of the major ad platforms.

PPC Hero

Coralogix announces $10M Series A to bring more intelligence to logging

November 26, 2019 No Comments

Coralogix, a startup that wants to bring automation and intelligence to logging, announced a $ 10 million Series A investment today.

The round was led by Aleph with participation from StageOne Ventures, Janvest Capital Partners and 2B Angels. Today’s investment brings the total raised to $ 16.2 million, according to the company.

CEO and co-founder Ariel Assaraf says his company focuses on two main areas: logging and analysis. The startup has been doing traditional applications performance monitoring up until now, but today, it also announced it was getting into security logging, where it tracks logs for anomalies and shares this information with security information and event management (SEIM) tools.

“We do standard log analytics in terms of ingesting, parsing, visualizing, alerting and searching for log data at scale using scaled, secure infrastructure,” Assaraf said. In addition, the company has developed a set of algorithms to analyze the data, and begin to understand patterns of expected behavior, and how to make use of that data to recognize and solve problems in an automated fashion.

“So the idea is to generally monitor a system automatically for customers plus giving them the tools to quickly drill down into data, understand how it behaves and get context to the issues that they see,” he said.

For instance, the tool could recognize that a certain sequence of events like a user logging in, authenticating that user and redirecting him or her to the application or website. All of those events happen every time, so if there is something different, the system will recognize that and share the information with DevOps team that something is amiss.

The company, which has offices in Tel Aviv, San Francisco and Kiev, was founded in 2015. It already has 1500 customers including Postman, Fiverr, KFC and Caesars Palace. They’ve been able to build the company with just 30 people to this point, but want to expand the sales and marketing team to help build it out the customer base further. The new money should help in that regard.

Enterprise – TechCrunch

User-Specific Knowledge Graphs to Support Queries and Predictions

November 26, 2019 No Comments

A recently granted patent from Google is about supporting querying and predictions, and it does this by focusing on user-specific knowledge graphs.

Those User Specific Knowledge Graphs can be specific to particular users.

This means Google can use those graphs to provide results in response to one or more queries submitted by the user, and/or to surface data that might be relevant to the user.

I was reminded of another patent that I recently wrote about when I saw this patent, in the post Answering Questions Using Knowledge Graphs, where Google may perform a search on a question someone asks, and build a knowledge graph from the search results returned, to use to find the answer to their question.

So Google doesn’t just have one knowledge graph but may use many knowledge graphs.

New ones for questions that may be asked, or for different people asking those questions.

This User-Specific Knowledge Graph patent tells us that innovative aspects of the process behind it include:

  1. Receiving user-specific content
  2. The user-specific content can be associated with a user of one or more computer services
  3. That user-specific content is processed using one or more parsers to identify one or more entities and one or more relationships between those entities
  4. A parser being specific to a schema, and the one or more entities and the one or more relationships between entities being identified based on the schema
  5. This processes provides one or more user-specific knowledge graphs
  6. A user-specific knowledge graph being specific to the user, which includes nodes and edges between nodes to define relationships between entities based on the schema
  7. The process includes storing the one or more user-specific knowledge graphs

Optional Features involving providing one or more user-specific knowledge graphs may also include:

  • Determining that a node representing an entity of the one or more entities and an edge representing a relationship associated with the entity are absent from a user-specific knowledge graph
  • Adding the node and the edge to the user-specific knowledge graph
  • The edge connecting the node to another node of the user-specific knowledge graph

Actions further include:

  1. Receiving a query
  2. Receiving one or more user-specific results that are responsive to the query
  3. The one or more user-specific results are provided based on the one or more user-specific knowledge graphs
  4. Providing the one or more user-specific results for display to the user
  5. An edge is associated with a weight
  6. The weight indicating a relevance of a relationship represented by the edge
  7. A value of the weight increases based on reinforcement of the relationship in subsequent user-specific content
  8. A value of the weight decreases based on lack of reinforcement of the relationship in subsequent user-specific content
  9. A number of user-specific knowledge graphs are provided based on the user-specific content
  10. Each user-specific knowledge graph being specific to a respective schema
  11. The user-specific content is provided through use of the one or more computer-implemented services by the user

Advantages of Using the User-Specific Knowledge Graph System

The patent describes the advantages of implementing the process in this patent:

  1. Enables knowledge about individual users to be captured in a structured manner
  2. Enabling results to be provided in response to complex queries, e.g., series of queries, regarding a user
  3. The user-specific knowledge graph may provide a single canonical representation of the user based on user activity inferred from one or more computer-implemented services
  4. User activities could be overlapping, where reconciliation of the user-specific knowledge graph ensures a canonical entry is provided for each activity
  5. Joining these together could lead to a universal knowledge graph, e.g., non-user-specific knowledge graph, and user-specific knowledge graphs

(That Universal Knowledge Graph sounds interesting.)

Information from sources like the following may be used to create User-Specific Knowledge Graphs:

  • A user’s social network
  • Social actions or activities
  • Profession
  • A user’s preferences
  • A user’s current location

This is so that content that could be more relevant to the user is used in those knowledge graphs.

We are told also that “a user’s identity may be treated so that no personally identifiable information can be determined for the user,” and that “a user’s geographic location may be generalized so that a particular location of a user cannot be determined.”

The User-specific Knowledge Graph Patent

This patent can be found at:

Structured user graph to support querying and predictions
Inventors: Pranav Khaitan and Shobha Diwakar
Assignee: Google LLC
US Patent: 10,482,139
Granted: November 19, 2019
Filed: November 5, 2013


Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving user-specific content, the user-specific content being associated with a user of one or more computer-implemented services, processing the user-specific content using one or more parsers to identify one or more entities and one or more relationships between entities, a parser being specific to a schema, and the one or more entities and the one or more relationships between entities being identified based on the schema, providing one or more user-specific knowledge graphs, a user-specific knowledge graph being specific to the user and including nodes and edges between nodes to define relationships between entities based on the schema, and storing the one or more user-specific knowledge graphs.

What Content is in User-Specific Knowledge Graphs?

The types of services that user-specific knowledge graph information could be pulled from can include:

  • A search service
  • An electronic mail service
  • A chat service
  • A document sharing service
  • A calendar sharing service
  • A photo sharing service
  • A video sharing service
  • Blogging service
  • A micro-blogging service
  • A social networking service
  • A location (location-aware) service
  • A check-in service
  • A ratings and review service

A User-Specific Knowledge Graph System

This patent describes a search system that includes a user-specific knowledge graph system as part of that search system, either directly connected to or connected to search system over a network.

The search system may interact with the user-specific knowledge graph system to create a user-specific knowledge graph.

That user-specific knowledge graph system may provide one or more user-specific knowledge graphs, which can be stored in a data store.

Each user-specific knowledge graph is specific to a user of the one or more computer-implemented services, e.g., search services provided by the search system.

The search system may interact with the user-specific knowledge graph system to provide one or more user-specific search results in response to a search query.

Structured User Graphs For Querying and Predictions

A user-specific knowledge graph is created based on content associated with the user.

These user-specific knowledge graphs include a number of nodes and edges between nodes.

A node represents an entity and an edge represents a relationship between entities.

Nodes and/or entities of a user-specific knowledge graph can be provided based on the content associated with a respective user, to which the user-specific knowledge graph is specific.

User-Specific Knowledge Graphs and Schemas

The user-specific knowledge graphs can be created based on one or more schemas (examples follow). A schema describes how data is structured in the user-specific knowledge graph.

A schema defines a structure for information provided in the graph.

A schema structures data based on domains, types, and properties.

A domain includes one or more types that share a namespace.

A namespace is provided as a directory of uniquely named objects, where each object in the namespace has a unique name or identifier.

For example, a type denotes an “is a” relationship about a topic, and is used to hold a collection of properties.

A topic can represent an entity, such as a person, place or thing.

Each of these topics can have one or more types associated with them.

A property can be associated with a topic and defines a “has a” relationship between the topic and a value of the property.

In some examples, the value of the property can include another topic.

A user-specific knowledge graph can be created based on content associated with a respective user.

That content may be processed by one or more parsers to populate the user-specific structured graph.

A parser may be specific to a particular schema.

Confidence or Weights in Connections

Weights that are assigned between nodes indicate a relative strength in the relationship between nodes.

The weights can be determined based on the content associated with the user, which content underlies provision of the user-specific knowledge graph.

That content can provide a single instance of a relationship between nodes, or multiple instances of a relationship between nodes.

So, there can be a minimum value and a maximum value.

Weights can also be dynamic:

  • Varying over time based on content associated with the user
  • Based on content associated with the user at a first time
  • Based on content or a lack of content associated with the user at a second time
  • The content at the first time can indicate a relationship between nodes
  • Weights can decay over time

Multiple User Specific Knowledge Graphs

More than one user-specific knowledge graph can be provided for a particular user.

Each user-specific knowledge graph may be specific to a particular schema.

Generally, a user-specific knowledge graph includes knowledge about a specific user in a structured manner. (It represents a portion of the user’s world through content associated with the user through one or more services.)

Knowledge captured in the user-specific knowledge graph can include things such as:

  • Activities
  • Films
  • Food
  • Social connections, e.g., real-world and/or virtual
  • Education
  • General likes
  • General dislikes

User-Specific Knowledge Graph Versus User-Specific Social Graph

A social graph contains information about people who someone might be connected to, where a user-specific Knowledge graph also overs knowledge about those connections, such as shared activities between people who might be connected in a knowledge graph.

Examples of Queries and User-Specific Knowledge Graphs

User-specific Knowledge graph example

These are examples from the patent. Note that searches, emails, social network posts may all work together to build a user-specific Knowledge Graph as seen in the combined messages/actions below, taken together, which may cause the weights on edges between nodes to become stronger, and nodes and edges to be added to that knowledge graph.

Example search query: [playing tennis with my kids in mountain view] to a search service

Search results: which may provide information about playing tennis with kids in Mountain View, Calif.

Nodes can be provided, with one representing the entity “Tennis,” one representing “Mountain View,” one representing “Family,” and a couple more each representing “Child.”

An edge can be provided that represents a “/Location/Play_In” relationship between the nodes, another edge may represent a “/Sport/Played_With” relationship between the nodes and other edges may represent “/Family/Member_Of” relationships between the node and the nodes.

Weights may be generated for each of the edges to represent different values as well.

A Person may post the example post “We had a great time playing tennis with our kids today!” in a social networking service, associated with geo-location data indicating Mountain View, Calif.

Nodes may be identified representing tennis, Mountain View, family and children, and edges between those nodes.

Weights may be generated between those edges.

Someone may receive an electronic message from a hotel, which says “Confirming your hotel reservation in Waikiki, Hi. from Oct. 15, 2014, through Oct. 20, 2014. We’re looking forward to making your family’s vacation enjoyable!”

Nodes can be added to the user-specific Knowledge graph, where those nodes represent the entities “Vacation” and “Waikiki”

Edges can be created in the user-specific knowledge graph in response to that email that represents a “/Vacation/Travelled_With” relationship between the nodes, one that represents a “/Vacation/CityTown” relationship between the nodes, and another edge that represents a “/Vacation/CityTown” relationship between the nodes.

Timing nodes may also be associated with the other nodes, such as a timing node representing October 2014, or a node representing a date range of Oct. 15, 2014, through Oct. 20, 2014.

The user can submit the example search query [kids tennis lessons in waikiki] to a search service.

Nodes may be created in the user-specific knowledge graph representing tennis, Waikiki, family, and children, as well as respective edges between at least some of the nodes.

That example search query may reinforce the relevance of the various entities and the relationships between the entities to the particular user.

That reinforcement may cause the respective weights associated with the edges to be increased.

The user can receive an email from a tennis club, which can include “Confirming tennis lessons at The Club of Tennis, Waikiki, Hi.”

Nodes represent tennis, and Waikiki, and the edges between them.

That email reinforces the relevance of the entities and the relationships between the entities to the particular user.

The weights between the entities could be increased, and a node could be added to represent the entity “The Club of Tennis,” which could then be connected to one or more other nodes.

User-Specific Knowledge Graphs Takeaways

This reminds me of personalized search, but tells us that it is looking at more than just our search history – It includes data from sources such as emails that we might send or receive, or posts that we might make to social networks. This knowledge graph may contain information about the social connections we have, but it also contains knowledge information about those connections as well. The patent tells us that personally identifiable information (including location information) will be protected, as well.

And it tells us that User-specific knowledge graph information could be joined together to build a universal knowledge graph, which means that Google is building knowledge graphs to answer specific questions and for specific users that could potentially be joined together, to enable them to avoid the limitations of a knowledge graph based upon human-edited sources like Wikipedia.

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NASA’s space pallet concept could land rovers on the moon cheaply and simply

November 26, 2019 No Comments

Establishing an enduring presence on the Moon will mean making a lot of landings — and NASA researchers want to make those landings as reliable and cheap as possible. This robotic “pallet lander” concept would be a dead simple (as lunar landers go) way to put up to 300 kilograms of rover and payload onto the Moon’s surface.

Detailed in a technical paper published today, the lander is a sort of space pallet: a strong, basic framework that could be a unit in many a future mission. It’s still a concept and doesn’t really have a name, so space pallet will do for now.

It’s an evolution of a design that emerged in studies surrounding the VIPER mission that was intended to “minimized cost and schedule” and just get the rover to the surface safely. In a rare admission of (at least theoretically) putting cost over performance, the paper’s introduction reads:

The design of the lander was based on a minimum set of level 1 requirements where traditional risk, mass, and performance trade parameters were weighed lower than cost. In other words, the team did not sacrifice ‘good enough’ for ‘better’ or ‘best.’

It should be noted, of course, that “good enough” hardly implies a slapdash job in the context of lunar landers. It just means that getting 5 percent more tensile strength from a material that costs 50 times more wasn’t considered a worthwhile trade-off. Same reason we don’t use ebony or elm for regular pallets. Instead they’re using the space travel equivalent of solid pine boards that have been tested into the ground. (The team does admit to extrapolating a little but emphasizes that this is first and foremost a realistic approach.)

The space pallet would go up aboard a commercial launch vehicle, such as a Dragon atop a Falcon 9 rocket. The vehicle would get the pallet and its rover payload into a trans-lunar injection trajectory, and a few days later the space pallet would perform the necessary landing maneuvers: attitude control, landing site selection, braking, and a soft touchdown with the rover’s solar panels facing the sun.

Once on the surface, the rover would go on its merry way at some point in the next couple hours. The lander would take a few surface images and characterize its surroundings for the team on Earth, then shut down permanently after 8 hours or so.

Yes, unfortunately the space pallet is not intended to survive the lunar night, the researchers point out. Though any presence on the moon’s surface is a powerful resource, it’s expensive to provide the kind of power and heating infrastructure that would let the lander live through the freezing, airless cold of the Moon’s weeks-long night.

Still, it’s possible that the craft could be equipped with some low-key, self-sustaining science experiments or hardware that could be of use to others later — a passive beacon for navigation, perhaps, or an intermittent seismic sensor that detect nearby meteorite impacts.

I’ve asked for a bit more information on the possibilities of science instruments onboard, and what the alternatives might be should the space pallet not be pursued further than concept stage. But even if that were to be the case, the team writes in the paper, “it is important to note that these and other derived technologies are extensible to other lander designs and missions.”

Gadgets – TechCrunch

Yext researches what American customers are looking for throughout the year

November 26, 2019 No Comments

Yext, the Search Experience Cloud company, released new research about American consumer search behavior during the past year. The data, drawn from a sample of more than 400,000 business locations in the United States, revealed new insights about when consumers are searching for and clicking most on businesses across retail, healthcare, financial services, and food, throughout the year.

Among the key findings:

  • Consumers are only getting more active in search: Consumer actions in business listings — driving directions clicks, clicks to call businesses, and more — grew 17% over the past year.
  • Search — and searchers — are getting better: Consumer actions in search grew faster (17%) than search impressions of business listings (10%) over the year, suggesting that customers are finding what they want faster. Whether searchers are learning to use more specific queries or search engines are getting better at understanding those queries, customers are spending less time searching and more time engaging with businesses.
  • Reviews are on the rise: Consumers are leaving more reviews about businesses. Review count per business location grew 27% over the year. In fact, financial services review volume grew 91% per location, the fastest growth of any industry. Businesses are getting savvier about the importance of reviews as well, responding to reviews 47% more than the year prior.

“Some industries are naturally more popular with consumers during certain seasons, but the need for businesses in every category to be in control of their facts online stays important year-round,” said Zahid Zakaria, Senior Director of Insights and Analytics at Yext. “By ensuring their information is accurate across channels — from the search results on their own website to their listings on third-party platforms — businesses can be prepared to capture the wave of customers who are interested in transacting with them, no matter what month it is.”

Yext analyzed when American consumers clicked online listings for various types of businesses throughout the year. The study found:

January | Resolving to stay healthy: With New Year’s resolutions fresh on their minds, and cold and flu season underway, Americans start the year off with visits to the doctor. In January, healthcare organizations see a 17% jump in clicks to their online listings relative to the previous month.

February | Money on their minds: In February and March, tax season is well underway and searches show it. Searching consumers engage with financial services institutions up to 11% more than the annual average.

March | Open house: Starting in March, consumers looking to ring in the season of renewal with a new home turn to search to find real estate agencies. Listings see a 22% average increase in clicks from February to May, complementing studies indicating that spring is a popular season for house hunting and selling.

April | Telecom phones it in: By April, the wave of consumers picking up the latest high-profile smartphone upgrades from the fall has subsided. During this month, clicks to phone carrier and telecommunications provider listings in search drop 14% compared to the month before.

May | May flowers and horsepower: In May, consumers look to capitalize on Memorial Day sales and revamp their rides in time for summer with an average 18% increase in clicks to automotive service search listings relative to the annual average.

June – July | Fun in the sun: Recreation and entertainment listings online — including theaters, sports venues, nightlife, and more — see a surge of consumer interest during the summer months, reaching an average 35% increase in clicks in July relative to the annual average. Clicks to hotel listings also bump up to 20% above the annual average during this time due to summer travel.

August | Back to school: School is just around the corner in August, and parents and students are not just stocking up on clothes, school supplies, gadgets, and other necessities, but also getting their cars in shape for the morning drop-off line at school. Clicks to listings for stores spike to 18% higher than the annual average. Educational services, like tutors and libraries, see clicks to listings increase 18% as well. Clicks to automotive service listings reach 21% above the annual average.

September | Falling into a Habit: As Americans wrap up their vacations and return to their school and work routines, clicks to recreation and entertainment listings take a noticeable dip (18% below the annual average) in September, falling up to 25% below the annual average in November.

October | Hitting the books: With the school year taking off by October, families get serious about grades again and search for tutors and other educational services. Clicks to listings in the education category see a nearly 10% jump relative to September.

November | Pass the Leftovers: During the month of Thanksgiving, hungry consumers prefer to eat in, with clicks to restaurant listings dropping 13% below the annual average.

December | Home for the holidays: In December, revelers celebrate the holidays with their families and opt to bunk with them over paying for lodging. During this month, clicks to hotel listings in search fall to 26% below the annual average.

December & January | The season of giving — and buying: Americans shopping for holiday gifts in December drive clicks to retail listings 11% more than the annual average. After the holiday shopping season ends in January, those clicks plummet an average of nearly 25% from December as consumers take a break from spending and recoup their savings.

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AWS expands its IoT services, brings Alexa to devices with only 1MB of RAM

November 25, 2019 No Comments

AWS today announced a number of IoT-related updates that, for the most part, aim to make getting started with its IoT services easier, especially for companies that are trying to deploy a large fleet of devices. The marquee announcement, however, is about the Alexa Voice Service, which makes Amazon’s Alex voice assistant available to hardware manufacturers who want to build it into their devices. These manufacturers can now create “Alexa built-in” devices with very low-powered chips and 1MB of RAM.

Until now, you needed at least 100MB of RAM and an ARM Cortex A-class processor. Now, the requirement for Alexa Voice Service integration for AWS IoT Core has come down 1MB and a cheaper Cortex-M processor. With that, chances are you’ll see even more lightbulbs, light switches and other simple, single-purpose devices with Alexa functionality. You obviously can’t run a complex voice-recognition model and decision engine on a device like this, so all of the media retrieval, audio decoding, etc. is done in the cloud. All it needs to be able to do is detect the wake word to start the Alexa functionality, which is a comparably simple model.

“We now offload the vast majority of all of this to the cloud,” AWS IoT VP Dirk Didascalou told me. “So the device can be ultra dumb. The only thing that the device still needs to do is wake word detection. That still needs to be covered on the device.” Didascalou noted that with new, lower-powered processors from NXP and Qualcomm, OEMs can reduce their engineering bill of materials by up to 50 percent, which will only make this capability more attractive to many companies.

Didascalou believes we’ll see manufacturers in all kinds of areas use this new functionality, but most of it will likely be in the consumer space. “It just opens up the what we call the real ambient intelligence and ambient computing space,” he said. “Because now you don’t need to identify where’s my hub — you just speak to your environment and your environment can interact with you. I think that’s a massive step towards this ambient intelligence via Alexa.”

No cloud computing announcement these days would be complete without talking about containers. Today’s container announcement for AWS’ IoT services is that IoT Greengrass, the company’s main platform for extending AWS to edge devices, now offers support for Docker containers. The reason for this is pretty straightforward. The early idea of Greengrass was to have developers write Lambda functions for it. But as Didascalou told me, a lot of companies also wanted to bring legacy and third-party applications to Greengrass devices, as well as those written in languages that are not currently supported by Greengrass. Didascalou noted that this also means you can bring any container from the Docker Hub or any other Docker container registry to Greengrass now, too.

“The idea of Greengrass was, you build an application once. And whether you deploy it to the cloud or at the edge or hybrid, it doesn’t matter, because it’s the same programming model,” he explained. “But very many older applications use containers. And then, of course, you saying, okay, as a company, I don’t necessarily want to rewrite something that works.”

Another notable new feature is Stream Manager for Greengrass. Until now, developers had to cobble together their own solutions for managing data streams from edge devices, using Lambda functions. Now, with this new feature, they don’t have to reinvent the wheel every time they want to build a new solution for connection management and data retention policies, etc., but can instead rely on this new functionality to do that for them. It’s pre-integrated with AWS Kinesis and IoT Analytics, too.

Also new for AWS IoT Greengrass are fleet provisioning, which makes it easier for businesses to quickly set up lots of new devices automatically, as well as secure tunneling for AWS IoT Device Management, which makes it easier for developers to remote access into a device and troubleshoot them. In addition, AWS IoT Core now features configurable endpoints.

Enterprise – TechCrunch

Celonis, a leader in big data process mining for enterprises, nabs $290M on a $2.5B valuation

November 25, 2019 No Comments

More than $ 1 trillion is spent by enterprises annually on “digital transformation” — the investments that organizations make to update their IT systems to get more out of them and reduce costs — and today one of the bigger startups that’s built a platform to help get the ball rolling is announcing a huge round of funding.

Celonis, a leader in the area of process mining — which tracks data produced by a company’s software, as well as how the software works, in order to provide guidance on what a company could and should do to improve it — has raised $ 290 million in a Series C round of funding, giving the startup a post-money valuation of $ 2.5 billion.

Celonis was founded in 2011 in Munich — an industrial and economic center in Germany that you could say is a veritable Petri dish when it comes to large business in need of digital transformation — and has been cash-flow positive from the start. In fact, Celonis waited until it was nearly six years old to take its first outside funding (prior to this Series C it had picked up less than $ 80 million, see here and here).

The size and timing of this latest equity injection is due to seizing the moment, and tapping networks of people to do so. It has already been growing at a triple-digit rate, with customers like Siemens, Cisco, L’Oréal, Deutsche Telekom and Vodafone among them. 

“Our tech has become its own category with a lot of successful customers,” Bastian Nominacher, the co-CEO who co-founded the company with Alexander Rinke and Martin Klenk, said in an interview. “It’s a key driver for sustainable business operations, and we felt that we needed to have the right network of people to keep momentum in this market.”

To that end, this latest round’s participants lines up with the company’s strategic goals. It is being led by Arena Holdings — an investment firm led by Feroz Dewan — with Ryan Smith, co-founder and CEO of Qualtrics; and Tooey Courtemanche, founder and CEO of Procore, also included, alongside previous investors 83North and Accel.

Celonis said Smith will be a special advisor, working alongside another strategic board member, Hybris founder Carsten Thoma. Dewan, meanwhile, used to run hedge funds for Tiger Global (among other roles) and currently sits on the board of directors of Kraft Heinz.

“Celonis is the clear market leader in a category with open-ended potential. It has demonstrated an enviable record of growth and value creation for its customers and partners,” said Dewan in a statement. “Celonis helps companies capitalise on two inexorable trends that cut across geography and industry: the use of data to enable faster, better decision-making and the desire for all businesses to operate at their full potential.”

The core of Celonis’ offering is to provide process mining around an organizations’ IT systems. Nominacher said that this could include anything from 5 to over 100 different pieces of software, with the main idea being that Celonis’s platform monitors a company’s whole solar system of apps, so to speak, in order to produce its insights — providing and “X-ray” view of the situation, in the words of Rinke.

Those insights, in turn, are used either by the company itself, or by consultants engaged by the organization, to make further suggestions, whether that’s to implement something like robotic process automation (RPA) to speed up a specific process, or use a different piece of software to crunch data better, or reconfigure how staff is deployed, and so on. This is not a one-off thing: the idea is continuous monitoring to pick up new patterns or problems.

In recent times, the company has started to expand the system into a wider set of use cases, by providing tools to monitor operations and customer experience, and to apply its process mining engine to a wider set of company sizes beyond large enterprises, and by bringing in more AI to its basic techniques.

Interestingly, Nominacher said that there are currently no plans to, say, extend into RPA or other “fixing” tools itself, pointing to a kind of laser strategy that is likely part of what has helped it grow so well up to now.

“It’s important to focus on the relevant parts of what you provide,” he said. “We one layer, one that can give the right guidance.”

Enterprise – TechCrunch

Tesla’s Cybertruck, an Aston Martin SUV, and More Car News This Week

November 25, 2019 No Comments

One design expert called Tesla’s electric pickup truck “horrifying.” And a black lab who drove in circles for an hour. 
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