Agile software development in a context of plan-based organizations

Agile software development has increasingly been used in the last fifteen years with the goal of improving traditionally time-consuming and rather non-user friendly process of developing software code. As implications of agile development and its impact on employees are still unclear, it is important to understand the benefits, opportunities and limitations of this development or collaboration mechanism. Thus, empirical evidence with implications for decision makers in the field of corporate policy and software development is an open research field. This master thesis analyzes the potentials of agile software development and how this approach can be used to support the development processes in companies, in terms of efficiency, shorter time-to-market as well as better customer fit of the developed products or services. By exploring some of the key features of different methods and processes, the potentials and limitations of the selected approaches are analyzed and linked to recent literature insights

Agile software development in a context of plan-based organizations

Agile software development has increasingly been used in the last fifteen years with the goal of improving traditionally time-consuming and rather non-user friendly process of developing software code. As implications of agile development and its impact on employees are still unclear, it is important to understand the benefits, opportunities and limitations of this development or collaboration mechanism. Thus, empirical evidence with implications for decision makers in the field of corporate policy and software development is an open research field. This master thesis analyzes the potentials of agile software development and how this approach can be used to support the development processes in companies, in terms of efficiency, shorter time-to-market as well as better customer fit of the developed products or services. By exploring some of the key features of different methods and processes, the potentials and limitations of the selected approaches are analyzed and linked to recent literature insights

Improving Mobility of Base Transceiver Station Locating Method using Telegram’s Application

One main problem that must be coped with by a telecommunication company (Telco) is the connection interference experienced by customers. When a cell phone number Pn in a region R suffers from a connection problem, the first step commonly taken by that Telco’s technician is to find the base transceiver station (BTS) among existing BTSs in R that is currently covering Pn. However, the proprietary tools used to locate the covering BTS can usually be accessed only from the regional office’s intranet with a specific IP address. Alternatively, a technician can use telnet to log in to a mobile switching center (MSC) server and search to determine whether Pn is being attached in a BTS that is registered in the related MSC server. However, this method is exhausting and inefficient because an MSC server usually registers hundreds to thousands of BTSs. This article proposes improving the efficiency, mobility, and interoperability of BTS location-finding by making use Telegram’s bot and command-line interfaces. Mobility and interoperability are improved because the proposed method can run both on PCs and smartphones. The proposed method is investigated experimentally at Telkomsel Ltd., a known Telco in Indonesia. This method requires only 30 seconds to locate the covering BTS, which is 20 times and four to seven times faster than manual telnet and the proprietary tool, respectively

MUSEUM ISDIMAN PALAGAN AMBARAWA SEBAGAI SUMBER DAN MEDIA PEMBELAJARAN SEJARAH SMA NEGERI 1 AMBARAWA

The purpose of this study was to determine: (1) the history and development of the Isdiman Palagan Ambarawa Museum, (2) the condition and management of historic objects in the Isdiman Palagan Ambarawa Museum, (3) utilization of Isdiman Palagan Ambarawa Museum collection as instructional history media and source in SMA Negeri 1 Ambarawa.This study used qualitative descriptive method, with the shape of a single case study. The data source of this research was the informant, places, events, archives, documents and historical objects Isdiman Palagan Ambarawa Museum. Data collection techniques used were observation, interviews, questionnaires, and analysis of documents. Footage used was purposive sampling techniques derived from Isdiman Palagan Ambarawa Museum manager, agents of history, history teacher, vice principal of curriculum, facilities and infrastructure vice principal, students, Dispora of Semarang District. The validity of the data used was triangulation techniques of data and methods. The data analysis technique used was interactive analysis, which was a process of analysis that move between the three components which include data reduction, data presentation, and verification or conclusion.This research indicate that: (1) the history and development of the Isdiman Palagan Ambarawa Museum was built on the basic of respect for Lieutenant Isdiman and has historical value in the event of the Battle Ambarawa, (2) the condition and management of historic objects in the Isdiman Palagan Ambarawa Museum still limited to administrative management were still modest, (3) the use Isdiman Palagan Ambarawa Museum collection as instructional history media and source of SMA Negeri 1 Ambarawa adjusted to the Standards Competency and Basic Competency listed on the KTSP (Education Unit Based Curriculum).

Is DevOps another Project Management Methodology?

In this paper, the authors aim to present the concept of DevOps (Development & Operations), considering its degree of novelty in the area of project management. Firstly, the authors will bring theoretical arguments to support the idea that DevOps is an early-stage methodology, built on the Agile principles, but coming with its own contributions in project management for software development and implementation. Therefore, we believe that after a short time, DevOps will replace its predecessors. Secondly, we experienced this methodology by developing a small project in academic environment by three teams of master students, using VersionOne software. The Conclusions will emphasize the relevance and the future expected effects of DevOps methodology in the project management domain

The quest for customer intelligence to support marketing decisions: A knowledge-based framework

The quest for customer intelligence to create value in marketing has highlighted the significance of the research focus of this paper. Customer intelligence, which is defined as understandings or insights resulting from the application of analytic techniques, plays a significant role in the survival and prosperity of enterprises in the knowledge-based economy. In this light, the paper has developed a framework of customer intelligence to support marketing decisions through the lens of knowledge-based theory. The proposed framework aims at supporting enterprises to identify the right customer data for the right customer intelligence corresponding with the right marketing decisions. In this light, four types of customer intelligence are clarified including product-aware intelligence, customer DNA intelligence, customer experience intelligence, and customer value intelligence. The applications of customer intelligence are also elucidated with relevant marketing decisions to maximize value creation. To illustrate the framework, an example is presented. The importance and originality of this study are that it responds to changes in customer intelligence in the age of massive data and covers multifaced aspects of marketing decisions

Deriving Classifiers with Single and Multi-Label Rules using New Associative Classification Methods

Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier’s predictive accuracy. Lastly, the vital problem related to web threats called “website phishing detection” was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings