Quartos Courses

Professional Education Resources


Welcome! to Quartos Online Courses

Quartos are peer reviewed, online learning modules that quickly bring you up-to-date on the latest developments in a specific technology. Take advantage of this new learning resource from the IEEE Computer Society.

Keep Your Training on the Leading Edge with Quartos

Short, Cost-Effective Training on Hot Topics Based on the Latest Research and Trends--Constantly Updated!


Earn 1.0 PDH (or 0.1 CEU) for each Quartos course completed.

  • The hottest technology topics impacting your future from the industry’s leading experts
  • Learn and test your knowledge in one fascinating, informative  “byte”
  • Advance knowledge and skills for only $29.95 nonmembers/$19.95 members
  • Constantly updated training on exactly the topics you need
  • Pick 3 and Pick 8 at special pricing for organizations
  • 1 professional development hour and .10 continuing education unit per Quarto
  • Ask about our special pricing for teams and organizations
  • Based on up-to-the-minute work by respected Computer Society authors, each course includes a downloadable PDF for your personal library!

See the Quartos difference here.

To learn more about how Quartos can keep your tech training on the leading edge, trainingpartners@computer.org to learn how to integrate Quartos into your current training program.

Big Data

From Data to Insight Work Practices of Analysts in the Enterprise

With greater availability of data, businesses are increasingly becoming data-driven enterprises, establishing standards for data acquisition, processing, infrastructure, and decision making. Enterprises now have people dedicated to performing analytic work to support decision makers. To better understand analytic work, particularly the role of enterprise business analysts, researchers interviewed 34 analysts at a large corporation. Analytical work occurred in an ecosystem of data, tools, and people; the ecosystem's overall quality and efficiency depended on the amount of coordination and collaboration. Analysts were the bridge between business and IT, closing the semantic gap between datasets, tools, and people. This article provides an overview of the analytic work in the enterprise, describing challenges in data, tools, and practices and identifying opportunities for new tools for collaborative analytics.

Distribution, Data, Deployment Software Architecture Convergence in Big Data Systems

Exponential data growth from the Internet, low-cost sensors, and high-fidelity instruments have fueled the development of advanced analytics operating on vast data repositories. These analytics bring business benefits ranging from Web content personalization to predictive maintenance of aircraft components. To construct the data repositories underpinning these systems, rapid innovation has occurred in distributed-data-management technologies, employing schemaless data models and relaxing consistency guarantees to satisfy scalability and availability requirements. These big data systems present many challenges to software architects. Distributed-software architecture quality attributes are tightly linked to both the data and deployment architectures. This causes a consolidation of concerns, and designs must be closely harmonized across these three architectures to satisfy quality requirements.

Cross-Layer Cloud Resource Configuration Selection in the Big Data Era

Cloud computing has transformed people's perception of how Internet-based applications can be deployed in datacenters and offered to users in a pay-as-you-go model. Despite the growing adoption of cloud datacenters, challenges related to big data application management still exist. One important research challenge is selecting configurations of resources as infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) layers such that big data application-specific service-level agreement goals (such as minimizing event-detection and decision-making delays, maximizing application and data availability, and maximizing the number of alerts sent per second) are constantly achieved for big data applications. This article discusses the issue of selecting resource configurations across multiple layers of a cloud computing stack by considering deployment of a real-time stock recommendation big data application over an Amazon Web Services public datacenter.

Big Data Privacy in the Internet of Things

The Internet of Things (IoT) enables data collection on a large scale, but the extraction of knowledge from this data can lead to user privacy issues. This article discusses privacy challenges in the IoT.

Big Data and Privacy: Emerging Issues

Specifically, the author examines the consequences of unevenness in big data, digital data going from local controlled settings to uncontrolled global settings, privacy effects of reputation monitoring systems, and inferring knowledge from social media.

Digital Data Grows into Big Data

With the proliferation of social media, which is largely fostered by the boom of the Internet and mobile ecosystems, a huge amount of multimedia data has been generated, forming the multimedia big data.

Multimedia Big Data Computing

With the proliferation of social media, which is largely fostered by the boom of the Internet and mobile ecosystems, a huge amount of multimedia data has been generated, forming the multimedia big data.

Sharpening Analytic Focus to Cope with Big Data Volume and Variety

The growing volumes of time-stamped data available from sensors, social media sources, Web logs, and medical histories present remarkable opportunities for researchers and policy analysts.


Application-Screen Masking A Hybrid Approach

Large organizations often face difficult tradeoffs in balancing the need to share information with the need to safeguard sensitive data. A prominent way to deal with this tradeoff is on-the-fly screen masking of sensitive data in applications. A proposed hybrid approach for masking Web application screens combines the advantages of the context available at the presentation layer with the flexibility and low overhead of masking at the network layer. This solution can identify sensitive information in the visual context of the application screen and then automatically generate the masking rules to enforce at run time. This approach supports the creation of highly expressive masking rules, while keeping rule authoring easy and intuitive, resulting in an easy to use, effective system.

Cyberhuman Security

The ways in which we use and develop tools shape human destiny. This is especially true when such tools are used in harmful ways--as with the recent rash of cyberattacks.

Denial and Deception in Cyber Defense

As attack techniques evolve, cybersystems must also evolve to provide the best continuous defense. Leveraging classical denial and deception techniques to understand the specifics of adversary attacks enables an organization to build an active, threat- based cyber defense.

Protecting Websites from Attack with Secure Delivery Networks

Secure delivery networks can help prevent or mitigate the most common attacks against mission-critical websites. A case study from a leading provider of content delivery services illustrates one such network's operation and effectiveness. The Web extra at https://youtu.be/4FRRI0aJLQM is an overview of the evolving threat landscape with Akamai Director of Web Security Solutions Product Marketing, Dan Shugrue. Dan also shares how Akamai's Kona Site Defender service handles the increasing frequency, volume and sophistication of Web attacks with a unique architecture that is always on and doesn't degrade performance.


Being a DevOps Developer

DevOps, the synergy between software development and IT operations, was an open secret before it became a mass movement. Passionate programmers were often also closet system administrators—sometimes literally so, by nurturing recycled hardware in their home’s closet.

Chaos Engineering

Modern software-based services are implemented as distributed systems with complex behavior and failure modes. Chaos engineering uses experimentation to ensure system availability. Netflix engineers have developed principles of chaos engineering that describe how to design and run experiments.


DevOps is about fast, flexible development and provisioning business processes. It efficiently integrates development, delivery, and operations, thus facilitating a lean, fluid connection of these traditionally separated silos. In this instalment of Software Technology, Gorka Gallardo, Josune Hernantes, Nicolas Serrano, and I present a brief overview of most recent DevOps technologies such as delivery tools and microservices and discuss what they mean for industry projects. I look forward to hearing from both readers and prospective column authors. —Christof Ebert

The Forgotten Architecture

Most software architecture books focus on building new systems. However, successful systems spend much more time running in their production environment than being initially developed. That’s why the DevOps movement’s recent emergence is so heartening. It emphasizes development and operations staff working together as early as possible—sharing tools, processes, and practices to smooth the path to production.

Making It Easy to Do the Right Thing

Wotif Group used DevOps principles to recover from the downward spiral of manual release activity that many IT departments face. Its approach involved the idea of "making it easy to do the right thing."

Microservices Architecture Enables DevOps

When DevOps started gaining momentum in the software industry, one of the first service-based architectural styles to be introduced, be applied in practice, and become popular was microservices.

Internet of Things

Internet of Things Making the Hype a Reality

The Internet of Things (IoT) will democratize knowledge. Organizations are looking for ways to create active knowledge and insight from IoT data and apply this data to new business models in which understanding and addressing customer needs and demands is key. To ensure that the IoT can meet this challenge, the author identifies six key interest areas.

Internet of Things Perspectives

This article offers expert opinion on two Internet of Things (IoT) perspectives. The first is a research agenda for the IoT to ensure the development of a trusted, secure, reliable, and interoperable net-centric computing environment. The second discusses the IoT as a human agent, extension, and complement. 

Learning Internet-of-Things Security "Hands-On"

What can you glean from using inexpensive, off-the-shelf parts to create Internet of Things (IoT) use cases? As it turns out, a lot. The fast productization of IoT technologies is leaving users vulnerable to security and privacy risks.

Identifying and Authenticating IoT Objects in a Natural Context

Internet of Things analytics engines are complex to use and often optimized for a single domain or limited to proprietary data. A prototype system shows that existing Web analytics technologies can successfully be repurposed for IoT applications including sensor monitoring and user engagement tracking.

Principles for Engineering IoT Cloud Systems

Engineering Internet of Things (IoT) and cloud services to provide a coherent software layer for continuous deployment, provision, and execution of applications for various domains is complex.

Repurposing Web Analytics to Support the IoT

A proposed property-aware name service simultaneously supports what, where, and when properties of each IoT object through unique, text-based, and human-readable identity assignments.

Healthcare Cybersecurity

Application Security through Federated Clouds

Although a wide range of organizations are adopting the cloud to deploy a variety of applications, security concerns keep many from deploying certain types of applications, such as healthcare and financial, in the cloud. This article explores how federated clouds can be exploited to meet applications' security requirements.

Trustworthy Processing of Healthcare Big Data in Hybrid Clouds

Managing large, heterogeneous, and rapidly increasing volumes of data, and extracting value out of such data, has long been a challenge. In the past, this was partially mitigated by fast processing technologies that exploited Moore's law.

Protecting Patient Data-The Economic Perspective of Healthcare Security

Despite the ambiguities of healthcare security costs and benefits, market mechanisms can nudge healthcare organizations toward effective proactive and voluntary security actions. However, the effectiveness of market mechanisms suffers from the economic forces of the imperfect US healthcare market. Thus, market-driven investments must be supplemented with regulator intervention across all types of healthcare organizations. However, such regulatory intervention should focus on reinforcing the economic impact of information security rather than simply trying to force specific behavior.

Data Protection in Healthcare Social Networks

Healthcare social networking sites (HSNSs) provide users with tools and services to easily establish contact with each other around shared problems and utilize the wisdom of crowds to attack disease. The increasing popularity of HSNSs has led to concern over the privacy of health-related data published through these websites. The open philosophy of contemporary HSNSs can result in unauthorized use and disclosure of sensitive personal health data. Prior research about how best to protect such data has focused on specific technologies such as privacy settings and data anonymization. However, the fundamental challenge to data protection in HSNSs is more system-related than technical. A set of system requirements with a special emphasis on preventing privacy violations by service providers can help tackle these privacy and security problems.

Applying KISS to Healthcare Information Technology

Current public and private healthcare information technology initiatives have failed to achieve secure integration among providers. Applying the "keep it simple, stupid" principle offers key guidance for solving this problem.

Risk-Based Security

Decision Support Software for Probabilistic Risk Assessment Using Bayesian Networks

Norman Fenton and Martin Neil explain the world of Bayesian decision networks and the development of the company Agena, which developed its own Bayesian network platform.

A Cloud Security Risk-Management Strategy

Given the threat of security breaches, to both cloud service providers and organizational cloud service users, cloud security and privacy are growing public policy concerns as well a salient area of inquiry for researchers. To ensure organizational competitiveness, cloud service providers and organizational cloud service users must make detailed preparations to act against cyberthreats before they occur, and to recover from malicious cyberactivities when such threats succeed. A cloud security risk-management strategy should be a dynamic document that's regularly reviewed by stakeholders, and should include policies and objectives that align with the organization's needs.

Cybersecurity Standards - Managing Risk and Creating Resiliences

A risk-based cybersecurity framework must continuously assimilate new information and track changing stakeholder priorities and adversarial capabilities, using decision-analysis tools to link technical data with expert judgment.

Vidal-Hall and Risk Management for Privacy Breaches

The recent English Court of Appeal case of Google v. Vidal-Hall raises three issues for many Internet-based businesses: whether they can be sued in tort for misuse of private information, whether browser-generated information is defined as personal data, and whether compensation for emotional distress without accompanying financial loss can be awarded.


Inferring Mobile User Status with Usage Cues

An online inference engine monitors and extracts usage cues from wearables and identifies whether the user is busy, alone, happy, or stressed. Experiments show 85 percent identification accuracy with negligible energy cost.


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