This new feature allows tracking investor activities in certain technology fields, for instance.  Consider the following example screenshot (yellow nodes: technology fields, pink nodes: financial institutions):

This graph was extracted based on news from a two-week time window.  The graph shows relations between several investors and technology fields.  For instance, there is a group of investors related to “big data” and “unstructured data” (FirstMark Capital, Greycroft Partners, and IA Ventures, at around 6 o’clock in the graph).  This group was extracted from news articles reporting on a $15mio financing round at NewsCred (http://www.newscred.com/).  One of these news articles is at PEHub (http://www.pehub.com/191725/191725/).

https://support.evernote.com/ics/support/KBAnswer.asp?questionID=2181&hitOffset=135+29+11+2&docID=23937

You can, for instance, set up a public notebook, create a feed for it, and add this feed to one of your mergeflow topic profiles.  In these topic profiles, you can then use your notebook entries as “topic anchors”, for instance.  In order to do this, go to a document from your Evernote notebook feed and click on “document context”.  This shows you if there is topically related material anywhere in your topic profile.

Inventor-company networks can be relevant for several reasons, for example:

• Co-inventor networks often reflect how teams in a company are organized.  If several people are co-inventors, it is likely that they work together as a team.  The patents of these co-inventors usually are topically related as well.
• Connections between companies may reflect cross-company collaboration.  E.g. if two companies are co-assignees of a patent, these two companies probably are involved in some type of collaboration (research and development partnership, joint venture, etc.).
• Some inventors are more “central” in a co-inventor network than other inventors (“central” = have stronger connections to other inventors, or be connected to more inventors than others).  These central inventors usually play an important role in a company’s research and development efforts.
• mergeflow.stream can analyze patents and non-patent documents in the same context.  For instance, a user can create one topic profile with patent update feeds, feeds from scientific and technological publications, and feeds with investor activity news.  This topic profile then provides a unified view on all these different types of sources.

https://chrome.google.com/webstore/detail/feed%20/eaiadkmgpppkonfodnepmoknpaphnlij?hl=en

With Feed+, you can generate search feeds (i.e. get Google+ updates for a certain search query), or get publicly available updates from Google+ user accounts.  You can then subscribe to these web feeds with mergeflow.stream.

-document one’s current knowledge of a topic

-define one’s goals for further topic analysis and monitoring

The starter checklist is best used when starting with a new analysis use case, technology field, or analysis in general.

Get the latest version of the checklist as PDF: mergeflow.stream_StarterChecklist

Get the latest version of the checklist as PDF: mergeflow.stream_SearchFeedURLGenerator

Search feed generator for users who are not logged in to mergeflow.stream

Users who are not logged in or have no mergeflow.stream account can use this site:

http://mergeflow.net/searchsite

Search feed generator for users who are logged in to mergeflow.stream

Users who are logged in can switch to the “manage” view of a topic profile and generate search feeds there.  This provides more options:

• there are previews of current feed results
• from feed result previews, you can add the feed to the topic profile

http://www.securelist.com/en/analysis/204792262/Red_October_Diplomatic_Cyber_Attacks_Investigation

According to Kaspersky, Operation Red October appears to be a relatively large-scale cyber-espionage operation.

We have a topic profile on mergeflow.stream that takes as input hundreds of cybersecurity-related information streams.  You can find this topic profile here:

http://mergeflow.net/profiles/research/cyber-security/docs

In order to filter for news pertaining to Operation Red October, go here:

http://mergeflow.net/profiles/research/cyber-security/docs?tagtype=rising&q=Red%20October

Starting from there, you can try out different view settings, e.g. to see tag clouds consisting of relevant objects (e.g. companies) or countries that mergeflow.stream identified in the news on Operation Red October.  You can also explore an interactive graph of objects and countries related to Operation Red October:

http://mergeflow.net/profiles/research/cyber-security/graph?tagcount=500&q=Red%20October&tagcat=Company|Country|GovernmentAgency|Organization|Software|Technology

If you want to dig deeper, you can use mergeflow.stream’s URL syntax.  A documentation of the URL syntax is here:

http://www.mergeflow.com/Content/mergeflow.stream_URLSyntax.pdf

In order to illustrate how mergeflow analytics software can be used for technology scouting, we have written up a case study.  The topic of the case study is GPGPU (General Purpose Computing on Graphics Processing Units).  GPGPU refers to the utilization of GPUs for computing tasks that traditionally are done by the CPU (Central Processing Unit).  More information on GPGPU can be found at Wikipedia, for instance: en.wikipedia.org/wiki/GPGPU

Applications of GPGPU include data processing for medical applications, genomics, proteomics, simulations, scientific computing, graph analysis, machine learning, cryptography, finance, and “big data” in general.

For our case study, we analyzed several thousand text documents (patents, scientific publications, research project descriptions, etc.) that relate to GPGPU.  We used our analytics software, mergeflow.stream and mergeflow.engine.  Here are some of our key findings:

• From research project descriptions, we could identify several companies working on multiple-application GPGPU technologies.  Such companies may be particularly interesting to investors (e.g. venture capitalists).
• We automatically extracted a network of GPGPU applications that shows how these applications relate to each other.
• Among others, we found applications of GPGPU in genomics (motif detection), cyber security (intrusion detection), energy (power plant data analysis), and pricing and finance (options pricing).
• In the data set we analyzed, machine learning is one of the R&D activities with greatest actionable relevance to many different areas of application.

Add a feed to a topic profile such that

• it is clear who added the feed
• it is clear who / what the feed source is

• estimate the activity or publication rate of a topic
• find source and search feeds for a topic

Get the latest version of the checklist as PDF: mergeflow.stream_GeneralSearchFeeds_NoLogin

Get the latest version of the checklist as PDF: mergeflow.stream_SciTechFeeds_NoLogin

Get the latest version of the checklist as PDF: mergeflow.stream_SearchFeeds_LoginRequired

Get the latest version of the checklist as PDF: mergeflow.stream_SourceFeedSubscription

• adding feeds to a topic profile — so that it is clear who added the feed and what the source of the feed is, in case this is not provided by the feed description)
• subscribing to search feeds from various search engines — so that all settings of the feed are correct
• topic screening — to estimate the activity or publication rate of a topic, and to find search and source feeds for the topic

The documentation is not complete and not intended as a substitute for the original Twitter API documentation but it should help you to get started.

For example, you might have a specific topic profile on big data analytics.  In addition, you might also have a more general topic profile scientific and technological publications.  Now the question is, how can you feed “big data analytics” news (but not all the other stuff) from sci tech publications into big data analytics?

You can do this with the following steps (please note that you have to be signed in to your mergeflow.stream account in order to be able to do this):

1. Go to your general topic profile (sci tech publications in this case).

2. Go to the ‘Topic Radar’ tab.

3. Create a topic radar there (in our example, the topic radar might contain terms like “big data analytics”, or simply “big data”).

4. Copy the URL of the topic radar feed to the clipboard (-> right-click -> “copy link address”).

5. Go to the more specific topic profile (here, big data analytics).

6. Add the topic radar web feed to the profile (-> ‘Manage’ -> ‘Add feed’).

Done.  Now, whenever you modify the topic radar in your general topic profile (e.g. by adding more terms to it), the changes will automatically affect what the topic radar feed delivers to your more specific topic profile.

For both Nabucco and South Stream news, we used our analysis software to extract names of organizations (e.g. companies) and relationships that developed between those organizations over the last two years.  This resulted in two networks of organizations, one for Nabucco and one for South Stream.

Here you can see the results (please click on the images for full-size views).  The size of the nodes refers to the volume of news on an organization:

Organizations related to Nabucco:

Organizations related to South Stream:

As you can see, South Stream managed to set up a much larger network than Nabucco.

More recently, there were more and more reports on problems with Nabucco, for instance:

Will EU Drop Caspian Pipedream? (The Diplomat)

Reports of Nabucco Pipeline’s Death Are Exaggerated (NASDAQ Community)

Of course, we do not know whether failing to develop more industry relations (i.e. a bigger network) caused these problems for Nabucco, or whether there were other reasons.  However, we think that our results are at least interesting enough to warrant more such analyses for other projects in the future.

For instance, if you select “Google” as a keyword in a topic profile with technology blogs, the number of remaining news tends to be quite large.  You can see this here:

http://mergeflow.net/profiles/news/tech_blogs/recent?mk=Google

When you visit this link, you see a tag cloud that is generated for all science and technology news tagged with the keyword “Google” (usually a fairly large number of news).  You can now drill down by selecting further keywords.  All keywords you select act as AND-filters.  The following link, for example, shows news from the same topic profile (technology blogs) tagged with “Google” and “open source” (of course, there might be no news in this view, depending on news flow on these topics):

http://mergeflow.net/profiles/news/tech_blogs/recent?mk=Google%7Copen%20source

You can also remove any of the selected keywords.  For example, the link below shows what you get when you remove the keyword “Google” (i.e. news from technology blogs tagged with “open source”):

http://mergeflow.net/profiles/news/tech_blogs/recent?mk=open%20source

We hope you find these new tag clouds useful.  Depending on how well these tag clouds work for our users, we might replace the existing tag clouds elsewhere in mergeflow.stream by these drilldown tag clouds as well.