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Usually whenever someone brings up the issue of call quality analytics, what they're referring to is related to some kind of sales or marketing workflow. However, telecommunications companies themselves have often turned to big data processing systems as a way of gaining key insights about their own networks. Considering just how large these organizations can be, it makes sense that they'd have to rely on specialized software capable of dealing with the surfeit of information that passes through them.

Worldwide wireless customer statistics really help to put this in perspective. Over 13.5 billion phone calls and another 23 billion text messages pass through the network every single day. Database experts have developed sophisticated on-premise number crunching solutions to tackle this volume.

Leveraging External Data Processing Frameworks

Nearly every major programming language has a veritable ecosystem of user-designed libraries that can tackle most of the problems technicians throw at them. While these weren't made with the needs of communications companies in mind, they're often given very permissible licensing structures that make it possible for them to be included in various in-house projects developed by on-site information technology teams. Standard XML parsing libraries make it possible to process databases with millions of records in a very short period of time.

While wireless carriers never used something as simple as a bubble sort algorithm to process call records, they once did tackle their data processing workflows in a surprisingly obtuse fashion. All completed calls and text messages were put into flat data structures that somewhat resembled spreadsheet files. As the number of total records increased, the logs would start to have so many lines that tools like grep or awk wouldn't even be able to handle them correctly.

Organizations that offer business phone service plans have by and large replaced these with tree sorted documents authored in standard markup languages. In most cases, business phone operators can run them through a compression subroutine that helps to further reduce the raw number of bytes that have to be tracked at any given time. Processor cycles are normally less costly than file system seeks, which makes this a worthwhile venture.

Deflating a Large Dataset

Reducing the overall number of storage faults needed to recover individual records can go a long way toward speeding up the rate of crunching such a huge numbers, especially if either parallel processing or distributed computing gets involved. These paradigms leverage the power of multiple physical hardware assets to do the job much more quickly than any one given machine could. The most popular way to do so is through the deflate algorithm, which combines LZ77 compression with Huffman encoding to strip XML databases down to size.

Storage optimization techniques are vital for telephone companies that have to keep track of every single packet that passes through their network. Deflate streams consist of a series of discrete blocks that start with a three bit header. This explains the way the following stream of bytes is stored without adding too much overhead. In fact, judicious use of prefix codes can actually help to further reduce the amount of data that has to be transferred.

How Expansion Codes Save Space

Encoding schemes can actually increase the amount of data in a set, at least at first. Huffman algorithms, for instance, will replace uncommon blocks with commonly used symbols and long strings. This makes it easier to compress such strings with a dictionary service. Schemes based around the Burrows-Wheeler block sorting system could potentially be even more aggressive. That's made them popular with bzip2-formatted tarballs on Unix systems.

Since Unix actually grew out of experiments conducted by telephone company researchers, it makes sense that these standards would continue to be widely used even in today's world of 5G networks. Ironically, they've actually been able to scale somewhat better than many newer formats. Tape archive files were initially developed in 1979, yet they continue to be the way that source code packages get distributed to most GNU/Linux implementations. As a result, they've essentially become a standard for XML storage when dealing with huge databases that have to be updated every single time someone sends out a text message to another customer.

No matter how strong of a storage matrix someone assembles, however, the technicians behind it will have to address issues caused by bad actors.

Privacy and Security Considerations

Sensitive user information is always going to find its way into something like a call record, but these records are paradoxically required to keep telephone networks running smoothly. A number of communications giants have sough to alleviate the obvious risks involved in collecting this data by emphasizing local storage and using strong encryption algorithms to prevent unauthorized access.

Researchers currently believe that most 512-bit encryption codes can't be cracked via a brute force attack, but the advent of new solutions like quantum computing could potentially allow for even the most secure keys to eventually be guessed. Engineers are working on a series of mitigation that might help to further scramble data, thus leaving it essentially impossible to recover beyond background noise even if a physical piece of storage media falls into the wrong hands.

Large communications firms have to manage a growing list of digital risks and massive amounts of data on a regular basis, but it seems like their technical crews have things under wraps for now.

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