Communications Antenna and Propagation (IRECAP)

International Journal on Communications Antenna and Propagation (IRECAP)

Contents

Design of a Personal Communication Device, Based in EEG Signals 88 by Jennifer Salguero, Óscar F. Avilés, Mauricio F. Mauledoux A Study of Dual/Triple-Band Microstrip-Fed Slot Antenna Design 95 for WLAN/WiMAX Communication Systems by T. Kueathaweekun FDTD-Based Analysis of High Speed Data Transmission 104 Along Interconnections Terminated with Noisy MOSFET by H. Sanhaji, N. El Ouazzani Data Warehouse Development for Customer WIFI Access Service 114 at a Telecommunication Company by Abba Suganda Girsang, Candrauji Wira Prakoso Assuring Integrity for Data in Transit in Real-Time Data-Warehousing Systems 125 by Imane Lebdaoui, Said El Hajji, Ghizlane Orhanou Triple Band CPW Fed Monopole Leaf Shaped Patch Antenna 135 by Vivek Singh, Brijesh Mishra, Akhilesh Kumar Pandey, Ankit Kumar Patel, Shekhar Yadav, Rajeev Singh Design of a Resonator Using Auxiliary Source by an Iterative Method WCIP 142 by Latifa Mouldi, Mohamed Karim Azizi, Lassaad Latrach, Ali Gharsallah, Henri Baudrand Conception of a Malleable Multiband Antenna Using RF MEMS Varactors 147 for WIMAX802.16d and X Band by M. Houssini, Abdel-Mehsen Ahmad, Zouhair El Bazaal, Hamzeh Hamieh, Ziad Noun Compliance to Broadcast Standards of Patented Broadcast Antennas: 154 the Circularly Polarized Antenna by Gerino P. Mappatao Information Hiding Using Group of Bits Substitution 162 by Aditya Kumar Sahu, Gandharba Swain Comparative Analysis of Fast Fourier Transform 168 and Discrete Wavelet Transform Based MIMO-OFDM by Oboyerulu E. Agboje, Olabode B. Idowu-Bismark, Augustus E. Ibhaze

International Journal on Communications Antenna and Propagation (I.Re.C.A.P.), Vol. 7, N. 2 ISSN 2039 – 5086 April 2017

Data Warehouse Development for Customer WIFI Access Service at a Telecommunication Company

Abba Suganda Girsang, Candrauji Wira Prakoso

Abstract – As the main public company in telecommunication and broadband business in Indonesia, PT XYZ has always tried to ensure adequate wifi access service to meet the needs of broadband consumers. This wifi service, called wifi-id, uses an Authentication, Authorization, Accounting (AAA) server by collaborating with the other network providers. This study was based on the need of PT. XYZ to present reports or data quickly when needed, especially when PT. XYZ needs to reconcile the total monthly customer usage with business partners in order to do billing revenue sharing partnership. The problem arises because the script of CDR (called data record), which represents the use of broadband consumers, is stored in the form of CSV file only. It makes it difficult to make a report of customer access which is needed by PT. XYZ. The suggested solution is to move the existing data into a more structured storage in data warehouse. Data warehouse development was done through a nine-step methodology designed by Kimball and Ross. Furthermore, the data can be analyzed using OLAP to present the data in a visual form such as report or dashboard. With this solution, PT. XYZ can process and present the report relatively faster. Additionally, PT. XYZ will also get more benefit from the data stored in the form of information. Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Data Warehouse, Radius Protocol, OLAP Analysis, Radius Server, Authentication Authorization Accounting, WIFI

I. Introduction In addition, the report obtained by using the script is static and cannot be easily adapted to the changing needs PT. XYZ is a state-owned company that is engaged in of the management. telecommunications services and networks in Indonesia. The solution offered in this case study is to set up or PT. XYZ serves more than 151.9 million customers move the data previously stored in the form of a CDR consisting of mobile customers (TSEL) with more than CSV file into a data warehouse. A call detail record 125 million and 25.8 million fixed subscribers. The (CDR) is a data record produced by telecommunication company provides a wide range of communication equipments consisting of call detail transaction logs. It services including telephone network interconnection, contains valuable information which can be used for multimedia, data and internet communication-related many purposes in several domains, such as billing, fraud services, satellite transponder leasing, leased line, pay- detection and analytical purposes. However, in the real TV and VoIP services. One of the services provided by world, these needs face a big data challenge. Billions of PT. XYZ is a WIFI access service, WIFI.ID. With CDRs are generated every day and the processing WIFI.ID product, PT. XYZ provides access for systems are expected to deliver results in a timely customers to be able to connect to the internet. manner [1]. In addition to being a solution to the PT. XYZ faces some obstacles in presenting a report problems, the data warehouse also provides more or data quickly when needed, especially when PT. XYZ benefits for the company. Many companies use data needs to reconcile the total monthly customer usage with warehousing technologies to get more effective and business partners related to billing revenue sharing faster reports. The attribute time-stamped bitemporal partnership. In other cases, the presentation of data or approach performs less join statement and less customer information also needs to be done quickly background operations in data warehouse. Therefore, when there is a discrepancy of customer usage recorded data warehousing reports are achieved in higher by the network interface utilization trends in the Network performance [2]. Data warehousing is not about the Management System. tools. Rather, it is about creating a strategy to plan, Currently, the customer usage data are stored in the design, and construct a data storage capable of answering form of a CSV file and data processing is done manually business questions. A good strategy is a process that is with the help of a shell script. Although by using the never really finished; A defined data warehouse manual way the data still can be presented, the process development process provides a foundation for reliability will take a long time and is prone to errors. and reduction of risk. This process is defined through

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Abba Suganda Girsang, Candrauji Wira Prakoso methodology. Reliability is pivotal in reducing the costs II. Related Work of maintenance and support [3]. The data warehouse provides relevant information to II.1. Remote Authentication Dial in User Service management and executives to make decisions. The data RADIUS is an authentication, authorization and warehouse applications can also display reports quickly accounting protocol widely used in network and precisely. environments. Safe, efficient, and scalable RADIUS The needs of decision-makers have increased client module is an important part for a network access constantly and require the integration of different data server (NAS) to provide access services [10]. RADIUS sources to present the data in a way contributing to has now been implemented for authenticating network determine the best alternative for the organization, access remotely using a connection such as dial-up, as among the alternatives available. That was in the well as Virtual Private Networking (VPN), WIFI, and previous times which require significant time that leads other devices. to the end of its need or changes in the circumstances of RADIUS packet format consists of Code, Identifier, decision-making and the loss of opportunities to change Length, Authenticator and Attributes as shown in Figure the organization. So, there is a need to create data 1. warehouses, which is one of the most important developments of databases [4]. Mining operational telecommunications data such as Call Detail Record (CDR), Customer Relationship Management (CRM) data, have great potentials for predicting customer behavior, the movement of customers, and many other business needs such as the analysis of market share, and the analysis of pricing strategy, etc (mandal). To be successful in the market, not only do telecom companies compete in terms of Fig. 1. RADIUS Packet Format price, they also have to develop their services based on their knowledge of customer needs which are achieved Code has a length of 1 byte (8 bits), used to from the utilization of Call Detail Record (CDR) and distinguish the type of RADIUS messages that are sent. customer demographics. All the data should be stored in RADIUS message types can be access request, access a data mart CDR [5]. accept, reject access and access challenge. Authenticator Data warehouse development was done through a has a length of 16 bytes, used to authenticate a response nine-step methodology designed by Kimball and Ross from the RADIUS server. Attributes length is not fixed, [6] and used supporting software. Further data analysis which provides authentication, authorization and was performed using OLAP and the data were presented information. The examples of RADIUS attributes in a visual form (report and dashboard), making it easier include username and password. for PT.XYZ in monitoring information and making RADIUS uses the concept of AAA (Authentication, decisions based on the available information. The OLAP Authorization, Accounting). The concept is described in and data mining (OLAP mining) are a mechanism which Figure 2. integrates on-line analytical processing (OLAP) with data mining so that mining can be performed in different portions of databases or data warehouses at different levels of abstraction at the user's finger tips. With rapid developments of data warehouse and OLAP technologies in database industry, it is promising to develop OLAP mining mechanisms [7]. Data warehouses [21]-[23] can assist management in analyzing the existing conditions in the long span of time, because the data warehouse can hold historical data so that management can see trends that occur from time to time. By utilizing the dashboard application, management can easily see the conditions that exist in the organization as presented in graphical forms [8]. Similar solution has ever been applied on an accreditation system in educational environment. The institution needs the comprehensive documents for giving the information accurately before reviewed by Fig. 2. Message Call Flow of RADIUS assessor. However, the data are generally derived from various sources, various types, not structured and Authentication and Authorization are a process where dispersed [9]. the user is identified by the AAA server before

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subscribers use the network. In this process, the customer Currently, data warehouse is typically not only asks for permissions to the Network Access Server designed for efficient processing of read only analysis (NAS) to use the network. NAS AAA server then asks queries over large data, allowing only offline updates at whether the customer is entitled to use the network or night, but also must support the increasing demands for not. In addition, the server also allocates any service that the latest versions of the data. In other words, the can be accessed by subscribers in the network. current data warehouse must be close to real time [12]. Authorization is done when the customer has been Real time data ware housing plays a vital role in the field declared eligible to use the network. This process is done of databases. Typically, real time data ware housing by sending an Access-Request which will then be in provides reflection of business in real time and it defines response to the Access-Accept, Access-Reject or Access- the system as the refection of business in real time. Also, Challenge. we can define real time data ware housing as it is the Access-Request is made by the RADIUS client that is process of delivering information regarding different sent to the server to forward requests from customers operations in business [13]. that require authentication and authorization for the trial Data warehouse is a decision support system in which connection. the data warehouse plays an important role in decisions In the Access-Accept, incoming customers are given made by the executive. In building, a data warehouse is access when the user is authenticated. The RADIUS inseparable from a process called ETL (extraction, server will often check that customers use only the transformation, and loading). ETL is not a one-time resources according to the request. For example, the user event. As data sources change the data, warehouse house may only access hotspots, and not to use the printer. will periodically update. ETL process must be designed Information about the user in the database can be stored for ease of modification [14]. locally on the RADIUS server or stored in an external According to Kimball, building a data warehouse storage such as LDAP or Active Director. must pass through nine steps called nine-step Access-Reject response is when a customer is denied methodology. The nine stages are: choosing the process, access to all resources in the network. The causes can choosing the grain, identifying and conforming to the include failure to provide proper identification or the dimensions, choosing the fact, storing pre-calculations in account name is wrong / inactive the fact table, rounding out the dimension table, deciding Access-Challenge is made if there is a request for the duration of the database, tracking slowly changing additional information from the customer, such as dimensions, deciding the query priorities and the query alternative password, PIN, token or card. models. After authentication and authorization process, the next stage is Accounting. Accounting is a process conducted by the NAS and AAA server that records all III. Proposed Method customer activities in the network, such as when The method in this study consists of some steps: (1) customers start using the network, when customers requirement analysis by collecting data and information terminate the connection, how long customers use the through interviews and observations of the system network, how much data are accessible to the customers information that is currently running. Requirement from the network, etc. Information obtained from the analysis is one of important tasks to ensure successful accounting process is stored in the AAA server and can data warehouse project. It collects and restructures base be used for various purposes, such as billing, auditing, information that establishes data warehouse design and and network management. development of front end application; (2) an analysis of the information needs to be presented to the II.2. Data Warehouse management; (3) designing the data warehouse using a dimensional approach [15]. The information is presented According to Kimball and Ross, database in dashboard and report which is generated by data management system is a computer application whose warehouse using Pentaho Business Analytics and main objective is to save, retrieve, and modify data in a Pentaho Community Dashboard Editor. very structured way. Data within the DBMS are usually The nine steps to construct data warehouse in this mutually shared with the application. company were conducted through the following stages: A data warehouse can be built using a top-down approach, a bottom up approach, or a combination of the two. The top-down approach starts with overall design III.1. Choosing the Process and planning. It is useful in cases where the technology The selected process is the process providing data is mature and well known, and where the business related to the desired information or report by PT. XYZ. problems that must be solved are clear and well In general, there are several processes undertaken before understood. The bottom-up approach starts with customers can use the WIFI service. In the WIFI system experiments and prototypes. This is useful in the early at PT. XYZ, every customer who wants to use the WIFI stage of business modeling and technology development service will do the process of User Authentication in [11]. advance as shown in Figure 3.

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CDR file generated by the AAA system. Grain from the table the fact is "each row of data in the Fact Table store one row of data from a CDR file". Each row of data is saving information of username, time of usage, duration of use, space usage, as well as the WAC, Access Point, and SSID used when using WIFI service.

Data Warehouse User Accounting CDR Files Fig. 3. Call flow User Authentication Fig. 5. Data Retrieved from CDR File The parameters used in this process include username, password, and other parameters. Username dim_user ID of WIFI Customer Once the user is authenticated and approved by the WAC dim_wac Wireless Access Controller used by customer system, then the customer will get an IP and can start AP dim_ap Access Point Used by customer SSID dim_ssid SSID used by customer using the internet service. Once the customer browses for the first time, the AAA system will start to record the user usage. This process is called the Accounting Users III.3. Identifying and Conforming the Dimension process as seen on Figure 4. Dimension is selected according to the needs of the data warehouse to the WIFI service. As mentioned earlier, data in the data warehouse were taken from a CDR file and each row of data in the CDR stores information of username, time of usage, duration of use, space usage, as well as the WAC, Access Point, and SSID used when using WIFI service as shown Table I.

TABLE I LIST OF DIMENSIONS Dimension Table name Description Time describe date, month, Date dim_date and year

Fig. 4. Call flow User Accounting III.4. Choosing the Fact

AAA will record every transaction service usage by This phase creates the fact table according to the customers in CDR file. In addition to customer usage process analyzed and selected grain. Each fact has data data, each row CDR also stores information of username, that can be calculated and then displayed as reports, WAC, Access Point and SSID used by each customer charts, and diagrams. As can be seen from Table II, the while using the WIFI service. fact is created based on the available information. This case study focuses on the processes by utilizing TABLE II CDR Accounting Users that were generated to build the USER ACCOUNTING FACT data warehouse in order to produce a report or Fact Table Name Description information relevant to the needs of PT. XYZ associated User Accounting fact_accounting id_dim_date with WIFI service. id_dim_user id_dim_wac id_dim_ap III.2. Choosing the Grain id_dim_ssid usage_kb_upload Selection of grain means determining what is usage_kb_download usage_duration represented in each row of records in the fact table. To obtain information or report that is accurate and in accordance with the needs of PT. XYZ, the III.5. Storing Pre-Calculation in the Fact Table determination of grain that will be taken should be appropriate. In this case study, as shown in Figure 5, data In this phase, the fact table must be re-examined to in the fact table store the data previously recorded on the create custom column which requires a calculation. Pre- calculation needs to be done to get the value

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usage_kb_total. Calculation was done by summing the Loading (ETL). ETL process will change the data On- attribute of usage_kb_upload and usage_kb_download. Line Transactional Processing (OLTP) into data On-Line Analytical Processing (OLAP) [16]. ETL system, more than any other parts of the data warehouse edifice, is a

III.6. Rounding out the Dimensions Table legacy system that needs to be maintainable and scalable In this phase, the re-examination in the dimension over a long period of time [17]. ETL is one of success table and the determination of the hierarchy of dimension keys for building data warehouse. The required data are attributes were done to simplify the analysis process as currently extracted from CSV CDR to the staging table shown Table III. This phase covers information such as a on data warehouse. It was followed by a process of list of descriptions of the dimension tables and the list of transformation from entity table to the entity data tables. Table III details dimension tables. warehouse. The process transformation might have some types. The types include copy, insertion, calculation TABLE III automatically removing, etc. This phase must provide the DIMENSION TABLES DETAILS entity data warehouse which is from the operational data. Dimension Attribut Date Type Remarks dim_date id_dim_date varchar(8) Primary Key TABLE IV Format: [yyyyMMdd] LIST OF DIMENSION AND SCD TYPE day_in_month int Day in month [1..31] Dimension Tipe SCD Keterangan Month varchar(8) Name of Month dim_date Tipe 0 Data in this dimension is static Format: [January] (unchanged) dim_user Tipe 1 Data modification in this dimension Year int Year Format: [2016…] will overwrite the existing data. Time int Jam [HH] dim_wac Tipe 2 Creates new record if modification dim_user id_dim_user int Primary Key happens on wac_name and user_name varchar(20) User name wac_location attribute dim_wac id_dim_wac int Primary Key dim_ap Tipe 2 Creates new record if modification wac_name varchar(10) WAC name happens on ap_name and wac_location varchar(20) WAC location ap_location attribute dim_ap id_dim_ap int Primary Key dim_ssid Tipe 1 Data modification in this dimension ap_name varchar(20) Access Point name will overwrite the existing data. ap_location varchar(20) AP Location dim_ssid id_dim_ssid int Primary Key ssid_name varchar(20) SSID name

III.7. Choosing the Duration of Database The selection of the duration of the database was adapted to the needs of historical information required by PT. XYZ. Basically, the duration of the database was made flexible, so that it can continue to evolve and new data can be continuously added. However, this initial development will use data of the last month (October 2016). The database used was the MySQL database.

III.8. Tracking Slowly Changing Dimension Fig. 6. Star Schema of Accounting WIFI

This scheme uses slowly changing dimension (SCD) Figure 7 shows that the ETL process for staging table, type two. If the data change or were updated, the new retrieved_cdr. The process splits a AP name and SSID one become a new record on table in data warehouse. So, information to be stored in separate field. Date value was the old data were still there on the table as history. parsed to get the hour, day, month, and year value. Pre- Table IV shows SCD in dimension tables. Based on calculation was done to get total usage by adding upload the results of data warehouse design that was done with a and download usage. nine-step design methodology, a star schema can be constructed with one fact table with some dimension tables as shown in Figure 6.

IV. Analysis Result IV.1. Extract, Transform, and Load

The integration process is well known in the Business Intelligence as a process of Extract Transformation Fig. 7. ETL Staging Table

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Value for date dimension was retrieved from unique if the User name had been stored earlier on dim_user date of entries on staging table as shown in Figure 8. The table. The process was then followed by loading the new values were then split to get separate day, month, and unique entry to dim_user table. year value before loaded to dim_date table.

Fig. 8. ETL Date Dimension Fig. 12. ETL User Dimension

ETL process of AP dimension, as shown Figure 9, Once all the dimension tables were set and staging was started by retrieving unique AP name value from table was ready, ETL process for fact table was staging table and checking if the AP name had been performed. The process was mainly retrieving each line stored earlier on dim_ap table. The process was then of data from staging table and looking up to dimension followed by loading the new unique entry to dim_ap tables to get the foreign key of respective value for each table. dimension. The foreign keys of the measures were For SSID dimension as shown Fig. 10, ETL process loaded to the fact table. was started by retrieving unique SSID name value from staging table and checking if the SSID name had been stored earlier on dim_ssid table. The process was then followed by loading the new unique entry to dim_ssid table.

Fig. 13. ETL Fact Table

Fig. 9. ETL Access Point Dimension IV.2. Designing OLAP Cube

OLAP Analysis was done to see customer consumption data from different dimensions (multidimensional). Cube OLAP schema was needed to build OLAP Analysis. In the OLAP Cube scheme, there is a hierarchy of dimension and measure can be in roll-up or drill-down in accordance with the data requirements of PT.XYZ. Fig. 10. ETL SSID Dimension The following scheme is an OLAP scheme for time dimension. The hierarchy of the time dimension (dim_date) consists of level year, month, day, and hour that allow users to drill-down or roll-up good customer usage data yearly, monthly, or daily as seen in Figure 14. Tables V and VI are descriptions and arrangement of dim_date hierarchy levels from the highest level to the

lowest level. Fig. 11. ETL WAC Dimension In addition to the time dimension OLAP schema, OLAP schema was also made for other dimensions. One As shown on Figure 11, WAC dimension ETL of them is the dimension username. process was started by retrieving unique WAC name Username hierarchy dimension based on only one value from staging table and checking if the WAC name level is based on the Username as seen Figure 15. With had been stored earlier on dim_wac table. The process these dimensions, the user can analyze service usage was then followed by loading the new unique entry to WIFI per username. dim_wac table. Tables VII and VIII are descriptions and arrangement Similar to other dimensions, ETL process of User dim_user hierarchy levels from the highest level to the dimension as shown Figure 12 was started by retrieving lowest level. unique User name value from staging table and checking

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Intuitive formulation of informative and computationally-efficient queries on big and complex datasets present a number of challenges. As data collection is increasingly streamlined and ubiquitous, data exploration, discovery and analytics get considerably harder. Exploratory querying of heterogeneous and multi-source information is both difficult and necessary to advance our knowledge about the world around us [18].

TABLE IX MEASURE USAGE BYTE

Measure Name UsageByte Fig. 14. DateWise Hierarchy Aggregator Sum TABLE V Table Source fact_acct HIRARKI DATEWISE Column Source usage_byte Data Type Numeric Hierarchy Name DateWise Type TimeDimension Table Source dim_date TABLE X Primary Key id_date MEASURE USAGE TIME All Member Name All Years Measure Name UsageTime Aggregator Sum TABLE VI Table Source fact_acct LEVELS OF DATEWISE HIERARCHY Column Source usage_time Level Column Source Type Level Type Data Type Numeric Year Year Integer TimeYears Month Month Integer TimeMonths After OLAP schemes and the measure have been Day Date Integer TimeDays Hour Hour Integer TimeHours defined, OLAP Analysis can be done using a cube that has been made. One of them is a time-based OLAP analysis. OLAP Analysis allows management to see trends in customer usage of WIFI bytes, whether the amount or long duration of time based on the time dimension as seen in Figure 16. The management can drill down from the level of year, month, date, up to an hour. They can also roll up to see great customer usage based on the dimensions of time at the level above such as from daily to monthly or Fig. 15. UserWise Hierarchy monthly to yearly.

TABLE VII USERWISE HIERARCHY Hierarchy Name UserWise Type StandardDimension Table Source dim_user Primary Key id_user All Member Name All UserName

TABLE VIII LEVELS OF USERWISE HIERARCHY Level Column Source Type Level Type UserName username String Regular

Besides OLAP schema for dimension tables, the process also needs to define a measure that will be used, which is Usage Byte as seen in Table IX and Usage Time as seen in Table X. Usage Byte provides customer usage information WIFI in units of bytes. If information from this measure is in aggregated, then the user can see the total usage customers based on the existing Fig. 16. OLAP Analysis by Time Dimension dimension, in bytes. Usage Time presents the amount of customer usage information WIFI in seconds. If From the analysis above, the data warehouse user can information from this measure is in aggregated, then the view the amount of customer usage by date and even user can see the total usage customers based on the certain hours. So, the information can be obtained based existing dimension in seconds. on what hour usually access subscribers increased or

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decreased. OLAP Analysis can also be performed based on a certain date as seen on Figure 19. For each report, on the dimensions of time and Wireless Access the management can determine the parameters of the Controller which allows management to see trends in reporting period using the year and month. customer usage of WIFI bytes, whether the amount or long duration of time based on the dimensions of time and name of the wireless access controller as seen on Figure 17.

Fig. 17. OLAP Analysis by Time and WAC Dimension

As the figure shows, the user can see which WAC has high utilization so that they can be more focused in terms of maintenance and capacity development. Another example is the OLAP analysis based on the Fig. 19. Usage Report by Username and AP Name dimensions of time and the Access Point that enables management to see trends in customer usage of WIFI From this report, data warehouse users can view any based on the dimensions of time and the name of the AP which has been accessed by the customer at any access point, as shown by Figure 18. given time. This information can be developed if it is integrated with AP location data. So, it can show the places that are frequently visited by customers, which ultimately can be used as customer profiling materials. In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement [19]. Reports may also be presented in the form of a pie chart. One of which is Usage Report by Top 10 WAC which allows management to view the list of the top 10 WAC used by customers when using the WIFI service, which is based on the amount of usage as

seen on Figure 20. Then, a report uses a line chart; one of Fig. 18. OLAP Analysis by Time and AP Dimension which is Usage Trend Report By Date of Access that allows management to see great customer usage trends With the analysis based on Access Point, the data by date WIFI access as seen on Figure 21. warehouse users can view any AP whose usage is high at The figure shows the trend of the customer access. any given time. Data warehouse users can search for anomalies that occur if there is a difference in total customer usage IV.3. Report and Dashboard Design reports with business partners. This information can also be used as a reference for Based on OLAP analysis, then reports and dashboards marketing strategies, such as giving discounts on a can be made to present the results of the analysis. For certain duration for increasing the amount of customer examples, Usage Report By Username and AP Name access. Another example is Usage Trend Report By Hour that allow management to view the usage details of WIFI of Access which allows management to see the trend of service by customers using a particular Access Point and customer WIFI access usage by hour of access.

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should do the maintenance at around 1pm to 5 am so that not many customers are affected. The way to organize together and manage multiple charts regarding the same subject of interests is on dashboards. If the information in dashboards is not static and can be changed based on parameter value selections, those dashboards are called dynamic. In business, they are often used because the information came from different sources and the volume of data is huge. Dynamic Dashboards are preferred by intermediate level of managers for giving a quick image by their business segment [20]. Another visual flash report is using the dashboard as shown in Figure 23. Dashboard allows management to view a brief overview of customer usage trends and user contributions based on Access Point, Wireless Access Controller, as well as the SSID in real time. The dashboard consists of three panels: which are Input Date, Total Usage, Usage Trend, and Usage Contribution panel. Panel Input Date defines the period of data to be displayed on the dashboard. Users can define the start and end of the period of the data using this panel as shown in Figure 24. Panel Total Usage in Figure 23(a) shows the number of total usage time of the WIFI service at PT. XYZ based on the three-period: an annual, monthly, and the period of data specified by the user of the dashboard. The Trend Panel Usage shows the trend of WIFI service usage by customers. Trends are presented in two forms: the usage amount by access date within the period specified and the usage amount trends based on access

time within the period specified, as shown in Figure Fig. 20. Usage Report by Top 10 WAC 23(b). Then, the Usage Contribution Panel shows the contributions of some components in WIFI service usage by customers. Customers can access the service WIFI by using an SSID on an Access Point that is addressed by a Wireless Access Controller. In this panel, the management of PT. XYZ can see Top 10 components contributing most to the WIFI service as seen in Figure 23(c).

Fig. 21. Usage Trend Report By Date of Access

Figure 22 shows the information that can retrieve customer behavior related to WIFI service. Usage increased from 10 AM and began to increase dramatically at 10 to 11 nights. From this information, it can be concluded that if PT. XYZ needs to perform Fig. 22. Usage Trend Report By Hour of Access maintenance of devices that requires downtime, they

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(a)

(b)

(c)

Figs. 23. Dashboard WIFI Service

V. Conclusion With a data warehouse, PT. XYZ can quickly present data or information that is helpful when necessary, such as when they will reconcile the use of a customer with a business partner or when there are anomalies in customer usage. In addition to being the solution for the existing problems, PT. XYZ could also benefit more from available data compared to only storing data in the form of CDR. The benefits include, PT. XYZ can dynamically compile a report that can be easily tailored to specific Fig. 24. Panel Input Date needs, such as serving breakdown of data usage per

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Abba Suganda Girsang, Candrauji Wira Prakoso

customer, per Access Point, or the combination of and Engineering, 2016, vol. 128, no. 1, p. 12020. customer and Access Point. [17] R. Kimball and M. Ross, The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons, Presentation of data can be done in two forms: in the 2013. form of a report in which information and structure are [18] S. S. Husain, A. Kalinin, A. Truong, and I. D. Dinov, “SOCR tailored to the needs, and data in a data warehouse Data dashboard: an integrated big data archive mashing medicare, dashboard so that users can monitor trends all the time. labor, census and econometric information,” J. big data, vol. 2, no. 1, p. 13, 2015. With regard to the performance, in practical, the more [19] Z. Zhao, S.-L. Shaw, Y. Xu, F. Lu, J. Chen, and L. Yin, data are stored in DB, the more time is needed to process “Understanding the bias of call detail records in human mobility a report or dashboard. This issue can be addressed by research,” Int. J. Geogr. Inf. Sci., vol. 30, no. 9, pp. 1738–1762, selecting proper data grain as needed, so that the number 2016. [20] L. I. A. Monica and others, “Customer Data Analysis Model of data that is going to be stored can be controlled. using Business Intelligence Tools in Telecommunication The recommendation for the next research is to Companies,” Database Syst. J. BOARD, p. 39, 2015. integrate the output information with trend analysis or to [21] Nabri, H., Ouazar, D., Hasnaoui, M., Spatial Data Warehouse forecast method besides giving historical data. This Modeling at the Watershed Scale. Part 1: Design Aspects, (2015) International Journal on Information Technology (IREIT), 3 (4), solution will be able to provide future forecasting. pp. 124-130. [22] Madraky, A., Othman, Z., Hamdan, A., Hair-Oriented Data Model for Spatio-Temporal Data Mining, (2015) International References Review on Computers and Software (IRECOS), 10 (1), pp. 90- 101. [1] M. Agung and A. I. Kistijantoro, “High performance CDR [23] Darmawan, D., Fernando, C., Gunawan, A., Ivandi, J., Data processing with MapReduce,” in Telecommunication Systems Warehouse Development Based on Cloud Computing Using IBM Services and Applications (TSSA), 2015 9th International Informix and IBM Cognos for Multifinance Industry, (2016) Conference on, 2015, pp. 1–6. International Review on Computers and Software (IRECOS), 11 [2] C. E. Atay and G. Alp, “Modeling and Querying (9), pp. 804-815. Multidimensional Bitemporal Data Warehouses,” Int. J. Comput. Commun. Eng., vol. 5, no. 2, p. 110, 2016. [3] D. Moriya and G. Gosawi, “A Roadmap: Designing and Construction of Data Warehouse,” Bin. J. Data Min. Netw., vol. Authors’ information 5, no. 1, pp. 22–25, 2015. Master in Computer Science, Bina Nusantara University, [4] S. H. A. Aloush, “The Role of Data Warehouse in Decreasing the Jl. Kebon Jeruk Raya No 27, Jakarta, Indonesia. Time of Decision Taking,” Aust. J. Basic Appl. Sci., vol. 9, no. 5, E-mails: [email protected] pp. 216–219, 2015. [email protected] [5] Ć. Dragana, D. Be, and N. Gospi, “A Call Detail Records Data Mart: Data Modelling and OLAP Analysis Data Modelling: the Abba Suganda Girsang is currently a lecturer Conceptual , Logical and Physical,” Comput. Sci. Inf. Syst., vol. 6, at Master in Computer Science, Bina Nusantara no. 2, pp. 87–110, 2009. University, Jakarta, Indonesia Since 2015. He [6] R. Kimball, M. Ross, B. Becker, W. Thornthwaite, and J. Mundy, got Ph.D. in 2015 at the Institute of Computer The Kimball Group Reader: Relentlessly Practical Tools for Data and Communication Engineering, Department Warehousing and Business Intelligence Remastered Collection. of Electrical Engineering, National Cheng Kung John Wiley & Sons, 2015. University, Tainan, Taiwan, He graduated [7] N. Shaik, W. Ullah, and G. Pradeepni, “OLAP Mining Rules: bachelor from the Department of Electrical Association of OLAP with Data Mining,” Am. J. Eng. Res., vol. Engineering, Gadjah Mada University (UGM), Yogyakarta, Indonesia, 5, no. 2, pp. 237–240, 2016. in 2000. He then continued his masters degree in the Department of [8] M. Sethi, “Data Warehousing and OLAP Technology,” Int. J. Computer Science in the same university in 2006–2008. He was a staff Eng. Res. Appl., vol. 2, no. 2, pp. 955–960, 2012. consultant programmer in Bethesda Hospital, Yogyakarta, in 2001 and [9] A. S. Sinaga and A. S. Girsang, “University Accreditation using also worked as a web developer in 2002–2003. He then joined the Data Warehouse,” in Journal of Physics: Conference Series, faculty of Department of Informatics Engineering in Janabadra 2017, vol. 801, no. 1, p. 12030. University as a lecturer in 2003-2015. His research interests include [10] F. Jian and N. Tian-zhu, “Design, Extension and Implementation swarm, intelligence, combinatorial optimization, and decision support of RADIUS Client,” Int. J. Futur. Gener. Commun. Netw., vol. 9, system. no. 5, pp. 181–188, 2016. [11] R. Kimball, M. Ross, and others, “The data warehouse toolkit: the Candrauji Wira Prakoso was born on 23 complete guide to dimensional modelling,” Nachdr.]. New York March 1987, has completed undergraduate [ua] Wiley, pp. 1–447, 2002. studies in Electronic Engineering Polytechnic [12] K. Kakish and T. A. Kraft, “ETL evolution for real-time data Institute of Surabaya, majoring in informatics warehousing,” in Proceedings of the Conference on Information engineering in 2009 and continued the Master Systems Applied Research ISSN, 2012, vol. 2167, p. 1508. degree in Bina Nusantara university majored in [13] M. Arif, “A Survey on Data Warehouse Constructions, Processes information technology from 2015 until 2017. and Architectures,” Int. J. u-and e-Service, Sci. Technol., vol. 8, Has 8 years professional experience as Systems no. 4, pp. 9–16, 2015. Engineer in a system integrator and multinational companies. Mostly [14] S. H. A. El-Sappagh, A. M. A. Hendawi, and A. H. El working on a Telecommunication service provider from Value Added Bastawissy, “A proposed model for data warehouse ETL Service area until Packet Switching core network. processes,” J. King Saud Univ. Inf. Sci., vol. 23, no. 2, pp. 91– 104, 2011. [15] R. Kimball and J. Caserta, The Data Warehouse? ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. John Wiley & Sons, 2011. [16] R. J. Salaki, J. Waworuntu, and I. Tangkawarow, “Extract transformation loading from OLTP to OLAP data using pentaho data integration,” in IOP Conference Series: Materials Science

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