Storefront 3.12: Authentication issue after the upgrade, Logons expired. When only single DDC is added for logon restriction.

This is due to the discrepancy on how to handle the situation of partial success in enumeration(meaning some sites return success, but some return failures), initially we decided to show end users as much as possible, thus in your customer deployment, the user vdi were enumerated even though the enumeration in other DDCs(where user logon is not allowed) failed. But later we decided to at least prioritize the authentication failures, that’s why it fails the entire enumeration.

So with 3.12 the behavior of adding all DDCs for user to logon is expected, if that’s not acceptable, you can suggest customer to configure user mapping, so enumeration for a particular user shall only contact specified sites/DDCs.


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Parallel Workflow for Approval Tasks Variabels

I do not need a solution (just sharing information)

Can anyone explain me were exactly i have to create variables that i can use it to Count approvals and rejects over all splitted Dialog Workflows. If i generate global variable i can not use it. i readed all forum discussions but i dont get it working. Can anyone explain me how simply merge and global data simply merge works ?



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How Graph Analytics Works: Six Degrees of Kevin Bacon

From a technical perspective, the term “graph analytics” means using a graph format to perform analysis of relationships between data based on strength and direction. That might be a bit hard to understand for the uninitiated, particularly when the traditional idea of data analysis brings up images of poring over spreadsheets—very big spreadsheets, when you’re looking at big data with petabytes or exabytes of data.

So let’s break down that definition piece by piece to offer some clarity. Assuming you know the general idea behind analytics, what is the difference when we add the word “graph” to it? Consider the general statement above:

“Using a graph format to perform analysis of relationships between data based on strength and direction.”

Drilling that statement down into individual pieces, we can look at segments to gain a greater understanding of the definition.

“Using a graph format”: The technical definition of a graph is the relationship between nodes (aka vertices or points) and edges (aka links or lines).

“Analysis of relationships”: Graph analytics excels at delivering insights from relationships. The visual nature of the method makes it much easier to identify unexpected relationships and derive insights faster and quicker than using, say, a tabular format of data. While you may be able to come to the same conclusion by analyzing, for example, a spreadsheet of data, a graph format can bring this about with far less effort. The phrase “a picture is worth a thousand words” essentially applies here, and with computing tools designed to maximize the capabilities of graph analytics, insights can be determined in much more efficient ways.

“Based on strength or direction”: If you consider data points to be nodes in a graph, then the edges connecting those points define the relationship between them. Thus, strength of relationship can be derived from the density of the edge (as in, two points have a dozen relationships, so it is denser than an edge with a single connection) as well as the direction of the edge (the visual layout of nodes can translate into spatial data, where physical distance offers insight into the node.)

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A Real-World Example of Graph Analytics

The type of insight provided by graph analytics doesn’t have to be a complex technical concept; in fact, one of the easiest ways to explain graph analytics is through a party game that pretty much everyone has played at one point or another: Six Degrees of Kevin Bacon.

If you’re one of the few people on the planet who’s never heard of it, the idea behind Six Degrees of Kevin Bacon came about in the 1990s based on the theory that every actor was connected to Kevin Bacon through six degrees of working relationships. When playing with friends, the challenge is to minimize the number of degrees connecting the actor to Kevin Bacon—functionally, this is the same thing as running a query via graph analytics.

Imagine every actor working in Hollywood as a node in a massive graph, with Kevin Bacon at the center of it. Edges are drawn for every film connection between actors. We want to run a query to find the relationships that connect Kevin Bacon to another random node (actor). For this example, let’s pick Pedro Pascal (Game of Thrones, The Mandalorian). Pascal’s lengthy list of high-profile work means that he shares the cast list with many other notable actors, creating nearly limitless paths for connecting to Kevin Bacon. However, the goal is to find the shortest path to Kevin Bacon.

To do this, we run a query that analyzes the various paths (or, if you’re doing this in a party setting, you just think really hard), ultimately generating this output:

  • Kevin Bacon (node) was in Crazy, Stupid, Love (edge) with Julianne Moore (node).
  • Julianne Moore (node) was in Kingsman: The Golden Circle (edge) with Pedro Pascal.

 How Graph Analytics Works: Six Degrees of Kevin Bacon

The website The Oracle of Bacon (no relation to Oracle, of course) is an online database project built around this game. It should be noted that the site’s database uses a graph algorithm known as breadth-first, and in this instance, the site would give this a Bacon Number of two, because there are two edges Thus, the shortest path to connect Kevin Bacon is through Julianne Moore. That’s a pretty easy example given that Pedro Pascal currently has a high-profile stature. But what if we pick someone who’s a little more obscure—someone whose career came about prior to Kevin Bacon’s breakout period in the 1980s? For this example, let’s use Wendell Corey, a character actor who worked in the 1940s, 50s, and 60s.

If we were using an analytics tool, we would submit a query to search for the number of relationships between Kevin Bacon and Wendell Corey. This produces a Bacon Number of three:

  • Kevin Bacon (node) was in Animal House (edge) with Tim Matheson (node).
  • Tim Matheson (node) was in The Apple Dumping Gang Rides Again (edge) with Audrey Totter (node).
  • Audrey Totter (node) was in Any Number Can Play (edge) with Wendell Corey (node).

 How Graph Analytics Works: Six Degrees of Kevin Bacon

Using a breadth-first search, even seemingly obscure connections can find relationships quickly and efficiently. How else can we use the Kevin Bacon example to demonstrate elements of graph analytics? The principle behind Six Degrees is to minimize the number of edges between nodes. To try different types of graph analyses, we can look at the strength of relationships. In this case, if the nodes are Kevin Bacon and other actors, we can assign a single edge for every time they are in a film together. Thus, the theoretical strongest relationship between Kevin Bacon and another actor comes down to the quantity of edges in their respective filmographies.

 How Graph Analytics Works: Six Degrees of Kevin Bacon

Distance between nodes offers another dimension in how data is analyzed. For example, if we place Kevin Bacon at the center of this, then the placement of nodes (coordinates) is based on how recently the film with Kevin Bacon was made. In this case, Jill Hennessey co-starred in the series City on a Hill, which was made in 2019, so her node in this analysis would be placed immediately next to Kevin Bacon at the center. Queen Latifah appeared in the film Beauty Shop with Kevin Bacon in 2005, which would place her node further out. With the nodes distributed this way, a more-refined analysis could be run based on connections made in the last ten years.

 How Graph Analytics Works: Six Degrees of Kevin Bacon

The context of the query can change as well if you take the focus off Kevin Bacon. Degree centrality, or the calculation of a node’s volume of relationships in relation to the largest volume of relationships (mathematically, a percentage calculated by node’s relationships divided by largest volume of relationships), can easily be determined using graph analytics—which shows us, by the way, that Kevin Bacon does not have the largest degree centrality among actors listed in IMDb. Consider the logistics of trying to calculate such a thing by analyzing data in a tabular format versus a graph format where nodes and edges naturally take care of a significant amount of the prep work.

Digging Deeper into Graph Analytics

Six Degrees of Kevin Bacon provides a fun and accessible example of graph analytics, but there’s much more to this method from both a functional and technical perspective. To learn more about the how, what, and why behind graph analytics, check out What Is Graph Analytics for an in-depth explanation.

For more about how you can benefit from Oracle Big Data, visit Oracle’s Big Data page—and don’t forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.


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DLP – False positives from SCCM ClientWebService

I need a solution

Hi, we’re running DLP Endpoint on a number of machines and I’m having issues trying to filter out false positives that are being generated by SCCM processes on our systems. I have a policy set up to identify and create incidents for credit card numbers, with the scope set to Narrow width. I’m seeing numerous incidents being generated that are false positives, example below.

Endpoint Location: On the Corporate Network 
Application: Microsoft Host Process For Windows Services    
URL: http://sccm-server.mydomain.local:8530/ClientWebService/client.asmx
Destination IP:  

The message body has 87 matches for what it beleives are credit card numbers, however upon inspection it’s definitely false positives:

<DriverVerVersion> 2814750890000385</DriverVerVersion><Class>{4D36E97D-E325…00:00Z</DriverVerDate><DriverVerVersion> 2814750890000385</DriverVerVersion><Class>{4D36E97D-E325…00:00Z</DriverVerDate><DriverVerVersion> 2814750890000385</DriverVerVersion><Class>{4D36E97D-E325…00:00Z</DriverVerDate><DriverVerVersion> 2814750890000385</DriverVerVersion><Class>{4D36E96C-E325…00:00Z</DriverVerDate><DriverVerVersion> 2814750890000385</DriverVerVersion>

In my Agent Configuration I’ve already added the SCCM server’s hostname and IP address to the Filter By Network Properties field in the formats below

IP Filters: -,

Domain HTTP Filters: -sccm-server.mydomain.local

However this doesn’t appear to have worked as I’m still getting events generated even after recycling the detection server and restarting the agents.

I’ve also tried editing the policy and adding an optional validator to the Credit Card policy to exclude beginning characters “<DriverVerVersion>” but when I try to save this it throws up an error as it contains non-digits.

What’s the best way to filter out this sort of traffic?




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