Autentikasi User Dengan Metode Single Sign-On Berbasis Windows Active Directory Pada PT. XYZ

To connect to the company network, a WPA2-PSK-based security system is in place, requiring users to input the company Wi-Fi password. One of the issues that arises is the difficulty in identifying the status of users attempting to access the network. Apart from security concerns regarding the network, the use of this security key allows non-employees to connect to the network using personal devices. As a solution to enhance the existing authentication system, security systems like RADIUS can be utilized. This system operates to mitigate threats to the network security. The process undertaken through the implementation of this NDLC method commences with identifying and designing network security authentication, progressing through the implementation phase until the design can be regularly utilized. With the introduction of a user authentication system employing a single sign-on method based on Windows Active Directory at XYZ Inc., users will find it easier to connect to the wireless network. With WPA2-Enterprise, access to the network will be restricted.Untuk terhubung ke dalam jaringan perusahaan terdapat sistem keamanan berbasis WPA2-PSK sehingga user diwajibkan memasukan password wifi perusahaan. Salah satu isu yang muncul adalah kesulitan dalam mengidentifikasi status user yang berupaya mengakses jaringan. Selain permasalahan dari segi keamanan jaringan, dengan pengunaan key security tersebut user selain karyawan dapat terhubung ke jaringan menggunakan perangkat pribadi. Sebagai salah satu solusi untuk memperbaiki sistem autentikasi yang ada pada saat ini, terdapat sistem keamanan yang dapat dimanfaatkan seperti RADIUS. Sistem dijalankan untuk menghindari ancaman pada sistem keamanan jaringan. Proses yang dilakukan dengan menerapkan metode NDLC ini dimulai dengan mengidentifikasi dan melakukan perancangan autentikasi keamanan pada jaringan sampai ke tahap implementasi rancangan tersebut hingga dapat digunakan secara rutin. Dengan adanya sistem autentikasi user dengan metode single sign-on berbasis Windows active directory pada PT. XYZ user akan lebih mudah untuk terhubung ke dalam jaringan wireless. Dengan WPA2 - Enterprise akses untuk masuk ke dalam jaringan akan terbatas

PROV-TE: A Provenance-Driven Diagnostic Framework for Task Eviction in Data Centers

Cloud Computing allows users to control substantial computing power for complex data processing, generating huge and complex data. However, the virtual resources requested by users are rarely utilized to their full capacities. To mitigate this, providers often perform over-commitment to maximize profit, which can result in node overloading and consequent task eviction. This paper presents a novel framework that mines the huge and growing historical usage data generated by Cloud data centers to identify the causes of overloads. Provenance modelling is applied to add contextual meaning to the data, and the PROV-TE diagnostic framework provides algorithms to efficiently identify the causality of task eviction. Using simulation to reflect real world scenarios, our results demonstrate a precision and recall of the diagnostic algorithms of 83% and 90% respectively. This demonstrates a high level of accuracy of the identification of causes

Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

Era big data menjadi sebuah fenomena yang menarik untuk di bahas oleh kalangan peneliti dan pengembang perangkat lunak, pengembangan aplikasi dan konsep pengelolaan data semakin banyak varian dan dukungan menjadikan kerangka big data dapat masuk kesetiap lini kehidupan, data yang tersusun baik secara singkronus maupun asingkronus, melibatkan mesin dan manusia dalam pengumpulan data menjadikan teknologi ini semakin sejalan dengan konsep Revolusi Industri 4.0 Dalam berbagai kajian di sajikan konsep dan kerangka kerja Big Data, dari kajian tersebut beberapa peneliti menyajikan lapisan dalam arsitektur Big Data, di mana masing masing lapisan memberi input bagi lapisan lain untuk dapat di olah menjadi bentuk yang siap saji di masyarakat, lapisan yang tediri dari pengumpulan data, penyimpanan data, pemrosesan data serta Analisa data, sehingga pada lapisan aplikasi penggunaan data dapat lebih maksimal di rasakan oleh pengguna. Dalam makalah ini di sajikan beberapa bahan studi literature yang di rangkum untuk mendapatkan penjelasan mengenai lapisan arsitektur Big Data yang dapat di kembagkan dan di terapkan pada bidang bidang penelitian lain

Viral Marketing for Smart Cities: Influencers in Social Network Communities

Social networks are used by cities primarily for announcing local-area events, but also for increasing engagement of citizens in votes and elections. Given the current plethora of heterogeneous social networks, city administrators can benefit from social networks to promote initiatives, which are important to a current smart city as well use them to discover future needs in order to manage resources more efficiently. Our focus in this paper is how we can adapt commercial and viral marketing techniques to smart city systems to influence the behavior, opinion and choices of citizens in order to improve their well being and that of the whole society as well as predicting future trends and events

The Linkage to Business Goals in Data Science Projects

Modern data analytics equips businesses to make data-driven decisions by revealing patterns and insights that enhance strategic planning, operational efficiency, and process optimization. Its applications encompass personalized marketing through customer segmentation, predictive modelling for fraud detection, and enhancing security. A significant methodology in this realm is the Cross-Industry Standard Process for Data Mining (CRISP-DM), where the Business Understanding phase aims to ensure data science projects align with overarching business goals. However, challenges arise when these business objectives are ambiguous, ill-defined, or evolving. The complexity of data analytics projects underscores the need for domain expertise and robust collaboration between data scientists, business stakeholders, and domain experts. The imperative is to bridge the technical and business perspectives, manage expectations, and define project scopes. The short paper at hand addresses the question how data analytic goals can systematically align with business objectives in data science projects. By incorporating methods from Enterprise Architecture Management, we propose a structured approach for goal determination in data science projects, ensuring business and data mining objectives are seamlessly integrated

Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet. Therefore, we have systematically reviewed the Big Data testing techniques evidence occurring in the period 2010-2021. This paper discusses testing data processing by highlighting the techniques used in every processing phase. Furthermore, we discuss the challenges and future directions. Our findings show that diverse functional, non-functional and combined (functional and non-functional) testing techniques have been used to solve specific problems related to Big Data. At the same time, most of the testing challenges have been faced during the MapReduce validation phase. In addition, the combinatorial testing technique is one of the most applied techniques in combination with other techniques (i.e., random testing, mutation testing, input space partitioning and equivalence testing) to find various functional faults through Big Data testing.Comment: 32 page

Predicting job execution time on a high-performance computing cluster using a hierarchical data-driven methodology

Nowadays, evaluating the performance of a vehicle before the production phase is challenging and important. In the automotive industry, many virtual simulations are needed to model the vehicle behavior in the best possible way. However, these simulations require a lot of time without the user knowing their runtime in advance. Knowing the required time in advance would allow the user to manage the simulations more effectively and choose the best strategy to use the available computational resources. For this reason, we present an innovative data-driven method to estimate in advance the execution time of simulations. Our approach integrates unsupervised techniques, such as constrained k-means clustering, with classification and regression algorithms based on tree structures. In this paper, we present an innovative and hierarchical data-driven method for estimating the execution time of jobs. Numerous experiments were conducted on a real dataset to verify the effectiveness of the proposed approach. The experimental results show that the proposed method is promising

A Combined Representation Learning Approach for Better Job and Skill Recommendation

Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to acquire few additional skills which are required to assume the new position. In this work, we created three types of information net- works from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can utilize the information from all three networks to jointly learn the representation of the jobs and skills in the shared k-dimensional latent space. In our experiments, we show that by jointly learning the representation for the jobs and skills, our model provides better recommendation for both jobs and skills. Additionally, we also show some case studies which validate our claims

Editorial for IEEE access special section on theoretical foundations for big data applications : challenges and opportunities

Big data is one of the hottest research topics in science and technology communities, and it possesses a great application potential in every sector for our society, such as climate, economy, health, social science, and so on. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, and manage. We can conclude that big data is still in its infancy stage, and we will face many unprecedented problems and challenges along the way of this unfolding chapter of human history