Mobile Call Details Analysis Essay

Analysis of Call Detail Records (CDR)
using Excel Addin

When "problem calls" occur infrequently but often enough, they can negatively affect productivity and efficiency of a thriving business. Such calls are often not reproducible or predictable. For example, when a call suddenly disconnects during the middle of a call or,

  • there is voice in one direction but not the other
  • there is echo, or noise, or level issues in one direction and not the other
  • there are signaling issues such as - "no wink", "no ringing or ringback", "no call connection"
  • "mid-call digits" are not passed reliably during an IVR transaction
  • digits are split or merged causing incorrect called or calling numbers

When such calls occur and users complain, engineers need data to analyze "why such a problem occurred", but the volume of calls presents a needle in a haystack condition. Analyzing the CDRs allows you to

  • Drill-down to problem calls and identify the cause
  • Monitor daily operations
  • Identify customer behavior
  • Network usage and performance monitoring or daily or weekly basis

CDR Analysis System

GL's CDR Analysis System is designed for capturing all calls and all events (including voice quality) during the call, on any network type such as TDM, IP, or Wireless. Signaling, alarm, and call capture over IP or TDM lines is performed with capturing tools such as PacketScan™ or T1 E1 Call Capture and Analysis.  The system allows one to understand and analyze the captured call detail records, troubleshoot call failures, and provide insight into the overall performance.
Additional tools such as Advanced Excel® Addins, can be used to drill down to specific call, play or download the voice file, and generate graphs.

The CDR AnalysisSystem permits,

  • Capture for extended periods of time from hours to months
  • Once calls are captured, a search for "calls of interest" can be performed while live capturing continues
  • Drill-down to problem calls for post analysis
  • Additionally, monitor daily operations
  • Identify customer and user calling patterns
  • Network usage and performance monitoring on a daily or weekly basis
  • Trunk sizing, utilization

Excel® as CDR Analysis Tool

Excel® has the capability to handle large volumes of structured data, in-built statistical features, and offers easy automation through VBA programming, with which you can extract useful information from the captured call detail records with a click-of-a-button. In some cases where the data is unstructured, and complex, Excel® is not preferable for data analysis.

GL’s Advanced Excel® Addin is available as a customized application with the GL's CDR Analysis System to leverage from the Microsoft® Excel's capability to handle large volumes of data, inbuilt statistics, graphical features, and automation. This tool is frequently used by many Carriers, and Service Providers to analyze CDR on various network platforms such as TDM, IP, or Wireless.

GL’s Advanced Excel® Addin reads the records stored in a structured format such as CSV. It offers several parameters from the records to get to the calls-of-interest (COI). The add-in uses a combination of Excel® Pivot Table and Advanced Filtering features to perform filtering on these parameters. The filtered records view is be facilitated using a customized Windows® application that organizes and displays the information from CDR in a way you want. Viewing CDRs in this format is very informative in itself. However, you can also design a custom Pivot Table and generate Pivot Charts linked to this table. This helps to analyse and summarize the required information in table or graphical forms. Once you have the PivotTable designed, use Excel® inbuilt VBA support to program and automate repetitive tasks.

Excel® Addin programs can be easily customized per customer requirements.  Searching for unique conditions, such as "no wink", “low MOS scores”, “call with specific digits” can be quickly performed during post analysis. Some typical case studies are described below.


CASE STUDIES

CASE STUDY #1 : Filter and select calls-of-interest, drill-down to problem calls, and view the call details.

With our CDR analysis tools, the CDR column headers can be extracted and summarized in a GUI to allow customer to choose to the filter parameter.

Some examples include –


CASE STUDY #2 : Ability to play the voice files of a call-of-interest from the local or ftp directory.

Our CDR analysis tools contain the information on the captured voice files path, and file name for every call. Using advanced Excel® addin, you can filter for the particular call-of-interest and play the voice files corresponding to this call using a 3rd party audio editing software (such as Goldwave). You can also customize the excel to download these voice files to a particular directory after playing using the audio editing software.

 

 

 

 


CASE STUDY #3 : Organizing CDR data from different applications into a single GUI

Different call monitoring applications may produce different type of analysis results pertaining to a single call. The data type, columns, and the structure may be different the records output. Such records can be imported to excel, called within Excel® using by identifying a key id that links all these results to a call. With Excel®, one can develop a custom GUI to display the records from different applications pertaining to a call and explore all the call details.

For example, the screenshot here is displaying a ISDN call capture records by different applications, one producing the call summary such as called number, calling number, timestamp, & detail call signaling events, while another application is producing the voice band analysis. All these have been accommodated into a single GUI using Excel® VBA for ease of monitoring.

 

 



CASE STUDY #4 : Find calls by 'Day of the Week' or by 'Hour of the Day' (24-hr period call analysis)

If you are interested in looking at the pattern of calls made throughout the day during the week, or the number of calls for one particular day, or looking at a call placed at a particular minute, then with GL's CDR Analysis tools you can summarize this data either in tabular format or graphical format or both. Similarly, one can also plot the total and average call duration per hour, per week, or month.

 

 

 

 

 


CASE STUDY #5 :Analyze quality and network performance over an All-IP network from the call detail records, voiceband statistics, and captured calls.

The structured report from our network monitoring software, such as PacketScan™, can be easily imported into Excel® using cusotm addin. The addin allows to filter the required calls, analyze CDRs using pivot tables and generate different charts, such as call volumes, call duration, call failure causes, conversational mos, listening mos, packet loss, and more. Brief list of charts that can be customized are listed below:

  • Call Volume and Duration over the day, week or month
  • Call failure causes, answered or unanswered calls
  • Session disconnect delay(SDD) ,Post dial delay (PDD)
  • MOS scores – conversational (CMOS), listening (LMOS)
  • Average packet loss, jitter, and delay

Buyer's Guide

Please Note: The XX in the Item No. refers to the hardware platform, listed at the bottom of the Buyer's Guide, which the software will be running on. Therefore, XX can either be ETA or EEA (Octal/Quad Boards), PTA or PEA (tProbe Units), UTA or UEA (USB Units), HUT or HUE (Universal Cards), and HDT or HDE (HD cards) depending upon the hardware.

Item No.Item Description
 T1 E1 CDR Analysis System
 (The system requires any of the below mentioned T1 or E1 platforms with Basic Software)
PTA031
PEA031
VBA032
VBA036
CDR032
MS Excel 2010
SA048
SA017B
SA007e
SA002b
Enhanced Call Capture Analysis for T1
Enhanced Call Capture Analysis for E1
Voiceband Analyzer
Traffic Analysis for VBA
Call Data Records
Advanced Filtering of Calls
Goldwave Software
RJ-48C Crossover Cables
RJ-48 Y  Bridge
Dual 75/120 Ohm Transformer
 Packet over TDM CDR Analysis System
 (The system requires any of the below mentioned T1 or E1 platforms with Basic Software)
PTA135
PTA136
VBA032
VBA036
CDR032
MS Excel 2010
SA048
SA017B
SA007e
SA002b
PPP and MLPPP Analyzer
Packet Analysis for PPP and MLPPP Analyzer
Voiceband Analyzer
Traffic Analysis for VBA
Call Data Records
Advanced Filtering of Calls
Goldwave Software
RJ-48C Crossover Cables
RJ-48 Y  Bridge
Dual 75/120 Ohm Transformer
 Packet over IP CDR Analysis System
PKV100
PTA136
PKV301
VBA032
VBA036
CDR032
MS Excel 2010
SA048
PacketScan
Packet Analysis for PPP and MLPPP Analyzer
LAN Switch w/Mirror Port
Voiceband Analyzer
Traffic Analysis for VBA
Call Data Records
Advanced Filtering of Calls
Goldwave Software
Related Software

XX020

Record/Playback File Software

XX019

Transmit/Receive File Utility Software

SA026

Adobe Audition Software

SA048

Goldwave Software

SA021

File Edit Software

VBA032

Near Real-time Voice-band Analyzer

 Related Hardware

PTE001

tProbe™ Dual T1 E1 Laptop Analyzer with Basic Analyzer Software

HDT001/HDE001

Legacy HD T1 or E1 (PCI) Cards with Basic Analyzer Software

HTE001

Universal T1/E1 Card with Basic Analyzer Software

UTE001

Portable USB based Dual T1 or E1 Laptop Analyzer with Basic Analyzer Software

 Back to Call Data Records Page

As the Internet has been the technological breakthrough of the ’90s, mobile phones have changed our communication habits in the first decade of the twenty-first century. In a few years, the world coverage of mobile phone subscriptions has raised from 12% of the world population in 2000 up to 96% in 2014 - 6.8 billion subscribers - corresponding to a penetration of 128% in the developed world and 90% in developing countries [1]. Mobile communication has initiated the decline of landline use - decreasing both in developing and developed world since 2005 - and allows people to be connected even in the most remote places of the world.

In short, mobile phones are ubiquitous. In most countries of the developed world, the coverage reaches 100% of the population, and even in remote villages of developing countries, it is not unusual to cross paths with someone in the street talking on a mobile phone. Due to their ubiquity, mobile phones have stimulated the creativity of scientists to use them as millions of potential sensors of their environment. Mobile phones have been used, as distributed seismographs, as motorway traffic sensors, as transmitters of medical imagery or as communication hubs for high-level data such as the reporting of invading species [2] to only cite a few of their many side-uses.

Besides these applications of voluntary reporting, where users install applications on their mobile phones in the aim to serve as sensor, the essence of mobile phones have revealed them to be a source of even much richer data. The call data records (CDRs), needed by the mobile phone operators for billing purposes, contain an enormous amount of information on how, when, and with whom we communicate.

In the past, research on social interactions between individuals were mostly done by surveys, for which the number of participants ranges typically around 1,000 people, and for which the results were biased by the subjectivity of the participants’ answers. Mobile phone CDRs, instead, contain the information on communications between millions of people at a time, and contain real observations of communications between them rather than self-reported information.

In addition, CDRs also contain location data and may be coupled to external data on customers such as age or gender. Such a combination of personal data makes of mobile phone CDRs an extremely rich and informative source of data for scientists. The past few years have seen the rise of research based on the analysis of CDRs. First presented as a side-topic in network theory, it has now become a whole field of research in itself, and has been for a few years the leading topic of NetMob, an international conference on the analysis of mobile phone datasets, of which the fourth edition took place in April 2015. Closely related to this conference, a side-topic has now risen, namely the analysis of mobile phone datasets for the purpose of development. The telecom company Orange has, to this end, proposed a challenge named D4D, whose concept is to give access to a large number of research teams throughout the world to the same dataset from an African country. Their purpose is to make suggestions for development, on the basis of the observations extracted from the mobile phone dataset. The first challenge, conducted in 2013 was such a success that other initiatives such as this one have followed [3, 4], and the results of a second D4D challenge were presented at the NetMob conference in April 2015.

Of course, there are restrictions on the availability of some types of data and on the projected applications. First, the content of communications (SMS or phone discussions) is not recorded by the operator, and thus inaccessible to any third party - exception made of cases of phone tapping, which are not part of this subject. Secondly, while mobile phone operators have access to all the information filed by their customers and the CDRs, they may not give the same access to all the information to a third party (such as researchers), depending on their own privacy policies and the laws on protection of privacy that apply in the country of application. For example, names and phone numbers are never transmitted to external parties. In some countries, location data, i.e., the base stations at which each call is made, have to remain confidential - some operators are even not allowed to use their own data for private research.

Finally, when a company transmits data to a third party, it goes along with non-disclosure agreements (NDA’s) and contracts that strongly regulate the authorised research directions, in order to protect the users’ privacy.

Recently, with the rise of smartphones, other methods of collecting data overcoming those drawbacks have been designed: projects such as Reality Mining [5], OtaSizzle [6], or Sensible DTU [7] consist in distributing smartphones to individuals who volunteered for the study. A previously installed software then records data, and these data are further used for research by the team that distributed the smartphones. This new approach overcomes the privacy problems, as participants are clearly informed and consent to their data being used. On the one hand, these projects gather very rich data, as they usually collect more than just call logs, but also bluetooth proximity data, application usage, etc…. On the other hand, the sample of participants is always much more limited than in the case of CDRs shared by a provider, and the dataset contains information on 1,000 participants at most.

Yet, even the smallest bit of information is enough for triggering bursts of new applications, and day after day researchers discover new purposes one can get from mobile phone data. The first application of a study of phone logs (not mobile, though) appeared in 1949, with the seminal paper by George Zipf modeling the influence of distance on communication [8]. Since then, phone logs have been studied in order to infer relationships between the volume of communication and other parameters (see e.g. [9]), but the apparition of mobile phone data in massive quantities, and of computers and methods that are able to handle those data efficiently, has definitely made a breakthrough in that domain. Being personal objects, mobile phones enabled to infer real social networks from their CDRs, while fixed phones are shared by users of one same geographical space (a house, an office). The communications recorded on a mobile phone are thus representative of a part of the social network of one single person, where the records of a fixed phone show a superposition of several social actors. By being mobile, a mobile phone has two additional advantages: first, its owner has almost always the possibility to pick up a call, thus the communications are reflecting the temporal patterns of communications in great detail, and second, the positioning data of a mobile phone allows to track the displacements of its owner.

Given the large amount of research related to mobile phones, we will focus in this paper on contributions related to the analysis of massive CDR datasets. A chapter of the (unpublished) PhD thesis of Gautier Krings [10] gives an overview of the literature on mobile phone datasets analysis. This research area is growing fast and this survey is a significantly expanded version of that chapter, with additional sections and figures and an updated list of references. The paper is organized following the different types of data that may be used in related research. In Section 2 we will survey the contributions studying the topological properties of the static social network constructed from the calls between users. When information on the position of each node is available, such a network becomes a geographical network, and the relationship between distance and the structure of the network can be analyzed. This will be addressed in Section 3. Phone calls are always localized in time, and some of them might represent transient relationships while others rather long-lasting interactions. This has led researchers to study these networks as temporal networks, which will be presented in Section 4. In Section 5, we will focus on the abundant literature that has been produced on human mobility, made possible by the spatio-temporal information contained in CDR data. As mobile phone networks represent in their essence the transmission of information or more recently data between users, we will cover this topic in Section 6, with contributions on information diffusion and the spread of mobile phone viruses. Some contributions combine many of these different approaches to use mobile phone data towards many different applications, which will be the object of Section 7. Finally, in Section 8 we will consider privacy issues raised by the availability and use of personal data.

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