Milvus aims to achieve efficient similarity search and analytics for massive-scale vectors. A standalone Milvus instance can easily handle vector search for billion-scale vectors. However, for 10 billion, 100 billion, or even larger datasets, a Milvus cluster is needed. The cluster can be used as a standalone instance for upper-level applications and can meet the business needs of low latency, high concurrency for massive-scale data. A Milvus cluster can resend requests, separate reading…

Tips

1. Don't try to put the cart before the horse: realize that efficient data preparation (and thus interoperable standards) and data quality, especially in the enterprise environment, are a basic requirement for…

In the “Ecology of Metrics,” I wrote about “alignment” being a type of metric; alignment can measure the extent to which an organization’s supply or capacity is matched against the demands or needs of the market.  For instance, in a call centre, it would be highly desirable to have agents available to respond to calls at “precisely” the same time that clients are making calls.  If alignment is off even by only 15 to 30 seconds, impatient clients might hang up and never call again.  Similarly…

When our customers ask us what the best data warehouse is for their growing company, we consider the answer based on their specific needs. Usually, they need nearly real-time data for a low price without the need to maintain data warehouse infrastructure. In this case, we advise them to use…

One of the first things we do after launching a website nowadays is connect to Google Analytics. A little bit down the road we’ll connect more “out-of-box” analytics tools to calculate funnels, retention, A/B tests, and more.

These tools are great and work fine until a company gets bigger and analytics requirements get more sophisticated. It’s time to set up a data infrastructure, which means selecting a data collection tool, ETL tool, data warehouse, and BI tool on top of…

The Statsbot team estimated LTV 592 times for different clients and business models. 

Customer lifetime value, or LTV, is the amount of money that a customer will spend with your business in their “lifetime,” or at least, in the portion of it that they spend in a relationship with you. It’s an important indicator of how much you can spend on acquiring new customers. For example, your customer acquisition cost (CAC) is $150, and LTV is…

Not a few big organizations find their databases (or data warehouses) crammed with a huge number of old data tables, sometimes tens of thousands of them, after many years of operation. People have already forgotten why they are created; these tables even have long been useless. But all are kept for fear of mistaken deletion, causing heavy operation and maintenance workload. Moreover, a large number of stored procedures feed data continuously to these tables, seriously consuming the…

A person will ordinarily search the contents of a database using matching keywords and tags.  Sophisticated databases might allow for filtering:  for example using NOT, AND, OR on a number of keyword strings such as both titles and product descriptions.  It is not normally possible to submit, say, a personality profile to a database - or a personality profile and a setting.  Searching for “serial murders subway terminals” might lead to event information about precisely this, apparent serial…

Introduction

There are endless discussions on the databases arena about which DBMS is best suited for operational or data warehousing analytics, which one is the most efficient for online transaction processing, or which one is suitable for semantic integration. Recently graph databases are growing in popularity, especially in the enterprise space, and perhaps that adds more headache on those vendors that try to differentiate from competition…

Relation, Relationship and Association

While most players in the IT sector adopted Graph or Document databases and Hadoop based solutions, Hadoop is an enabler of HBase column store, it went almost unnoticed that several new DBMS, AtomicDB previous database engine of …

I find that different types of surveys represent a large source of data for many organizations: client questionnaires; recruitment interviews; incident debriefings; interrogations; borehole drilling surveys; quality control checks; marketing surveys; security and patrol logs; and inventory audits. I believe that for many people, the idea of collecting information using surveys makes sense; and they recognize the need for the data. Problems arise in relation to the transition from survey to…

The 3Vs model is the foundation of big data - Volume, Velocity, and Variety. It is used to express the key features of big data problems - for me, this is about to change. Big Data is not just about size, speed, or formats, the contextual enrichment is the most critical factor of how we unmask the best value out of data. How well you bring seemingly unrelated data together and identify the valuable connections determines how much power you unleash from your…

How many times a day do we ourselves, or hear someone else, utter the phrase “Google it”? It’s hard to imagine that a phrase so ubiquitous and universally understood has been around for less than two decades. The word “Google” has become synonymous with online search, and when we think about why this, it’s because Google yields the most relevant, comprehensive results, quickly. Essentially, it has changed the way we find and interact with content and information.

We’ve seen the…

Organizations are struggling with a fundamental challenge – there’s far more data than they can handle.  Sure, there’s a shared vision to analyze structured and unstructured data in support of better decision making but is this a reality for most companies?  The big data tidal wave is transforming the database management industry, employee skill sets, and business strategy as organizations race to unlock meaningful connections between disparate sources of…

In my previous blog on the Hopscotch and Robots simulation environment, I discussed the use of structural data extracted from hypothetical and real-life organizational events. In the current blog, I will be briefly covering conceptual issues more focused on the nature of the structural data itself including its theoretical significance.

Structural data holds information about the relationship between events.…

This is the third article in a series.  The first article is available here. The second article is available here.

Not every database requires a temporal database implementation, but some do. We can help…

As a long-term member of the Linked Data community, which has evolved from W3C's Semantic Web, the latest developments around Data Science have become more and more attractive to me due to its complementary perspectives on similar challenges. Both disciplines work on questions like these:

• How to extract meaningful information from large amounts of data?
• How to connect pieces of information to other pieces in…