Showing posts with label artificial intelligence. Show all posts
Showing posts with label artificial intelligence. Show all posts

0 HiveFire Curata Cuts the Work in Content Aggregation

Summary: HiveFire Curata makes it easy to assemble and republish content on specialized topics, attracting visitors to your company’s Web site.

Here’s an irony for you: the world is awash with content, but marketers struggle to find enough of it. It’s like a sailor dying of thirst.

Of course, sailors really do die of thirst. It happens when they’re surrounded by salt water they can’t drink. Marketers have the same problem: they can’t use most of the content that’s available.

HiveFire Curata aims to solve this problem by making it easier for marketers to extract usable content from the surrounding ocean. In fact, Curata provides a complete system to not just locate the right content but also to organize and present it to the marketer’s target audience. The goal is to make finding and repurposing existing content easier than creating new content on your own.

More specifically, Curata lets marketers build Web sites that republish content on selected topics, such as news of a particular industry. This attracts the marketer’s target customers and positions the marketer’s firm as an authority in the field. Once the audience is assembled, the site can also deliver the company’s own content and advertisements.

The trick to making this work is efficiency. You don’t need a special tool to scan the Internet: a simple Google Alert or Twitter search will do that for free. But you’d still need to read each article, tag it with keywords, and post it to your site. The work adds up so quickly that most marketers can’t afford to do it.

Curata reduces this effort by using natural language processing to automatically identify, classify and tag potential articles. It then presents them for manual review before being posted to a Curata Web site, which automatically adds them to appropriate indexes for future reference. The result is an organized archive that offers real value to someone interested in a topic. Because the search and tagging are highly automated, Curata says a typical client processes 40 to 80 articles each day in about 20 minutes.

Setting up a Curata site requires little technical skill. Users choose a format and then use a page designer to place widgets for articles, blog posts, lists of articles by category, author or entity, news streams, site search, media galleries, subscriptions and user registration. They also define the sources and search terms and exclusions the system will use to find content. Sources can include social media, news feeds, patent registrations and RSS subscriptions. Content on the Web site can also be published through RSS subscriptions, email newsletters, Twitter, Facebook and LinkedIn.

Because the system is hosted by Curata, it can be set up and maintained without help from the corporate IT department or Web team. This is a critical advantage for many marketers who lack priority access to those resources.

This is all good, and many companies should find Curata well worth the $1,500 per month ($1,200 with an annual contract). But I did see a number of features I’d like added. These include:

- screening the selected articles. Currently the system presents the articles in the sequence they are found, without identifying redundancies or even removing exact duplicates. Intelligent screening could remove some articles and present similar ones together, saving considerable labor when large volumes are involved.

- ranking the selected articles. The system currently reports the traffic attracted by each article, but it doesn’t use this to predict the popularity of new articles. Such predictions should be well within the capabilities of the natural language engine. Nor does the system rank articles on other criteria such as the authority of the source. Ranking could let editors review the most important articles first and discard the others once they had reached their daily quota.

- more subscriber information. Visitors register with the system to post comments and subscribe the email newsletters. But the profile cannot be extended beyond name, password and email address. This is missing an obvious opportunity to capture more information about potential leads.

- subscriber behavior tracking. Curata doesn’t report on the behavior of individual visitors, such as which items they view or how often they visit. This is another bundle of information that marketers and salespeople could use to understand visitor interests and to identify hot prospects.

HiveFire was open to these ideas when we discussed them, so I’d expect to see some appear in the future. But it's worth noting that Curata already has about 40 clients, who are presumably satisfied enough the existing features to pay for them. So even in its current state, Curata is worth a look if you want to sail the seas of content aggregation.

0 Saffron Technology Organizes Data into Memories

Summary: Saffron Technology provides an analytical database that explores relationships among entities and their attributes. It can explore networks, find similarities and guide decisions. Saffron is considerably more flexible than standard semantic engines.

As I was preparing my June 3 review of link analysis vendor Centrifuge Systems, Centrifuge introduced me to their business partners at Saffron Technology. This is an interesting product in its own right.

Saffron describes itself as an “associative memory base product,” a phrase that definitely takes some explaining. In simplest terms, Saffron organizes information into sets of three or four related items.

More specifically, it stores pairs of items in the context of an entity. For example, “Jack-climb-hill”, “Jack-plant-beanstalk”, “Jack-jump-candlestick” and “Jack-build-house” are all part of what Saffron calls Jack’s “memory”.

Related sets can be grouped into "matrices" that share another item. Thus, one matrix could contain “Jack-Jill-up-hill”, “Jack-Jill-fetch-water”, “Jack-Jill-fall-down” and “Jack-Jill-break-crown”, while “Jack-beanstalk-plant-seed”, “Jack-beanstalk-climb-up”, “Jack-beanstalk-steal-harp” and “Jack-beanstalk-kill-giant” form a separate matrix.

Saffron physically prejoins the data in Jack’s memory so it can be accessed easily and shared elements can be stored only once. Jill’s memory is stored separately from Jack’s even though some sets contain the same data. Jill’s memory may also contain sets without Jack (but let’s not tell him). Depending on how the system is configured, the hill could have its own memory as well.

Saffon’s approach lets it handle the subject-verb-object triples used in semantic analysis. But unlike standard semantic triplestores, Saffron is not limited to this structure. Sets could contain all nouns (Jack-Jill-hill), which can be useful even if the precise relationship among the items isn’t known. Or one of the items could be a time dimension. The system also counts of how often each item pair occurs in the source data, supporting statistical as well as semantic analysis.

These features make Saffron substantially more flexible than a semantic system. They also let it work with many more data sources, since reliable subject-verb-object relationships are often unavailable.

This may sound pretty dry, but Saffron's actual applications have been cloak-and-dagger stuff like looking for terrorists and finding roadside bombs. Remember that I was introduced to Saffron by Centrifuge, whose link analysis system is used primarily for law enforcement and security investigations. Saffron’s approach works particularly well with the Centrifuge front-end.

The fundamental advantage of Saffron is that associations among different items are directly accessible for analysis. This lets the system support different types of queries including:

- connections (identifying relationships among items)
- networks (showing how entities connect with each other)
- analogies (finding entities with similar connections)
- classifications (placing similar entities into groups)
- trends (reporting how connections change over time)
- episodes (finding patterns that repeat over time)

Real-world applications extend beyond link analysis to classifications and decisions. For example, the system can select medical treatments for a particular patient or assign suspended loans to different collection processes. These are complex processes. The system must identify entities with similar characteristics, identify the treatments and outcomes for those entities and estimate the likely outcomes of applying different treatments to the current entity.

Saffron's advantage with such applications is that the characteristics, treatments and outcomes are all just items in the data store. There's no need to load them differently or build a causal model of how they interact. In other words, the system needs no inherent assumptions about how the world works. This lets it effortlessly incorporate new data, uncover hidden relationships and react to new situations.

Well, “effortlessly” is a bit of an exaggeration. Saffron does some pretty complicated calculations to decide which entities are most similar and which treatments have the highest expected value (i.e., outcome value x outcome probability). Users have to review results and make judgments to tune the system. But this is still much less work than conventional statistical modeling or rule-based systems.

Saffron’s technology lets users define the types of data to will store, load data into the system, and execute API calls for the different types of analysis (connections, networks, analogies, etc.) Saffron generally relies on external systems to identify entities within source data, classify them into the specified categories, and report their associations to Saffron. Saffron can load structured data as well.

Saffron runs on a 64 bit “soft appliance” that distributes its data over clusters of server drives. The company claims world-record performance at ingesting, storing and accessing triplestore data, as well as compression to about 20 bytes per triple compared with 50 to 150 bytes per triple in other triplestore systems.

But let’s not get too carried away: Saffron works with large but not gigantic databases. Customer systems have been in the one-terabyte range.

Pricing for Saffron is starts at $125,000 per server, where a standard server is an eight core machine with 16 gigabytes of RAM per core. A trial version of the system is also available on the Amazon EC2 cloud as SaffronSierra.