Digital platforms can use application programming interfaces (APIs) to support third-party development of new apps and achieve growth at an unprecedented scale. However, there is also a dilemma between original new de...
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Digital platforms can use application programming interfaces (APIs) to support third-party development of new apps and achieve growth at an unprecedented scale. However, there is also a dilemma between original new development and copycatting by third-party suppliers. Motivated by this tension, we examined how APIs provided by digital platforms may influence two types of third-party new app development: original apps and app copycatting. We also investigated how these influences are dependent on app market conditions. We empirically tested our theoretical conjectures using data on a leading web browser platform, and applying analytics techniques on app source code to identify original apps and copycat apps. Based on a difference-in-differences identification strategy, our findings suggest that the provision of platform APIs enhance the original new development of apps. While platform APIs may facilitate app copycatting as well, our findings suggest that platform APIs can enhance app suppliers' relative attractiveness to original new development in comparison to copycatting. The enhancing effect of platform APIs on original new development is strengthened by app market potential and high market-level app complexity. The enhancing effect of platform APIs on app copycatting is strengthened by app market potential and high market concentration. Our study has important theoretical and practical implications.
During the coronavirus disease-2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) supplemented traditional COVID-19 case and death reporting with COVID-19 aggregate case and death surveilla...
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During the coronavirus disease-2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) supplemented traditional COVID-19 case and death reporting with COVID-19 aggregate case and death surveillance (ACS) to track daily cumulative numbers. Later, as public health jurisdictions (PHJs) revised the historical COVID-19 case and death data due to data reconciliation and updates, CDC devised a manual process to update these records in the ACS dataset for improving the accuracy of COVID-19 case and death data. Automatic data transfer via an applicationprogramming interface (API), an intermediary that enables software applications to communicate, reduces the time and effort in transferring data from PHJs to CDC. However, APIs must meet specific content requirements for use by CDC. As of March 2022, CDC has integrated APIs from 3 jurisdictions for COVID-19 ACS. Expanded use of APIs may provide efficiencies for COVID-19 and other emergency response planning efforts as evidenced by this proof-of-concept. In this article, we share the utility of APIs in COVID-19 ACS.
This article focuses on the role web application programming interfaces (APIs) play in the television (TV) industry's social media efforts. Web APIs are coding interfaces that allow different databases of informat...
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This article focuses on the role web application programming interfaces (APIs) play in the television (TV) industry's social media efforts. Web APIs are coding interfaces that allow different databases of information to communicate with one another. The widespread implementation of web APIs as a standard for information sharing offers a way for TV companies to more easily cobble together a presence in connected viewing environments. In this light, web API acts as, what Joshua Braun calls, `transparent intermediaries' that actively shape the range of possibilities available in these types of intermedial partnerships. I explain web APIs before showing how they relate to media studies through the TV show Psych's forays into designing social media experiences. Additionally, I will explain how web API-connected digital environments are used to facilitate what Tiziana Terranova calls soft control as web APIs embody Web 2.0 ideologies that celebrate information sharing between businesses that track audience attention.
Despite their rapid growth, the utilisation of application programming interfaces (APIs) poses challenges for companies under pressure to yield productive systems integration. APIs of larger systems tend to be large, ...
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Despite their rapid growth, the utilisation of application programming interfaces (APIs) poses challenges for companies under pressure to yield productive systems integration. APIs of larger systems tend to be large, complex and have reduced modularity and quality, which makes them cumbersome to comprehend and use. These challenges can be addressed by static API analysis that focuses on studying API code itself and deriving business entities and dependencies from operational signatures. However, existing techniques for static analysis of APIs face the challenges in deriving a sufficient coverage of business entity relationship types from implementation-oriented API operational signatures carrying limited semantic insights. The paper aims to address such problems by supporting static analysis techniques for APIs that improve their modularity. Our approach adopts an object-oriented paradigm where the concept of "object" is exemplified by the notion of business entity. It systematically applies interface analysis methods and techniques for eliciting knowledge of business entities and their attributes, for deriving the temporal order of calling operations across multiple business entities, and for learning and extracting various ways of invoking a service via APIs. The approach is implemented as an open-source tool and applied to a group of widely-deployed services in practice for validation. The research contributes to identifying key aspects of both the structure and behaviour of APIs, which will lead to building a simplified but comprehensive interface (presentation) layer to assist service users in understanding complex and overloaded interfaces as well as to facilitate efficient and effective service integration.
In the rapidly evolving landscape of Industry 4.0, the Asset Administration Shell (AAS) is a fundamental building block for developing digital twins. This paper presents an innovative approach to automatically generat...
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ISBN:
(数字)9798350363012
ISBN:
(纸本)9798350363029
In the rapidly evolving landscape of Industry 4.0, the Asset Administration Shell (AAS) is a fundamental building block for developing digital twins. This paper presents an innovative approach to automatically generate application programming interfaces (API) for AAS-based systems from a formalized specification. So far, the convention was to manually specify the API for AAS by tedious translation of the abstract specification published in form of a book. We propose, instead, to formalize the abstract specification, and automatically translate it into API. We thus streamline the development process, ensure effectiveness, and minimize the risk of errors in the representation of digital twins otherwise inherent in the manual procedure. Our proposed approach places great importance on automating the development of AAS APIs, using common and widespread YAML/OpenAPI as a serialization format due to its clarity and simplicity. Additionally, the paper examines other Interface Descriptions such as Protobuf, SOAP, GraphQL, and WSDL, emphasising the significance of clearly defined interfaces for efficient AAS API utilisation. Our method guarantees scalability and flexibility, specifically in relation to SDK generation, whereby different AAS interfaces are generated to support smooth integration within the constantly changing landscape of Industry 4.0. This comprehensive approach facilitates the entire lifecycle of digital twin development, spanning from API generation to SDK development, to ensure resilient and adaptable solutions agnostic to particular serialization formats.
application programming interfaces (APIs) for connecting applications are the most important for interoperability between disparate information systems today. It allows that the application that offers such an interfa...
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ISBN:
(数字)9798350387681
ISBN:
(纸本)9798350387698
application programming interfaces (APIs) for connecting applications are the most important for interoperability between disparate information systems today. It allows that the application that offers such an interface does not allow direct access to the server and all data, but each interface provides only the corresponding necessary data. The efficiency and speed of APIs enable information systems to retrieve formatted data that can be sequentially processed and used. In this paper API security will be discussed as a challenge today. The integration of today’s applications takes place in the conditions of a changing environment of information systems and growing threats of cyber defense and security. The new approach to security was created, which is reflected through the principles of Zero Trust Architecture (ZTA). To enable a comprehensive overview of API security challenges, in this work, the authors designed and presented a new extensive conceptual non-hierarchical model of API cyber defense. In addition to known cybersecurity threats, it takes into account the threats inherent in non-compliance with the principles of ZTA which is also known as Zero Trust Security Model, or Zero Trust Network Access (ZTNA). The designed model covers, amongst others, the intersection between the strategy of secure API construction and Zero Trust Architecture.
Information hiding is one of the most important and influential principles in software engineering. It prescribes that software modules hide implementation details from other modules in order to decrease the dependenc...
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ISBN:
(纸本)9781581138108
Information hiding is one of the most important and influential principles in software engineering. It prescribes that software modules hide implementation details from other modules in order to decrease the dependency between them. This separation also decreases the dependency among software developers implementing modules, thus simplifying some aspects of collaboration. A common instantiation of this principle is in the form of application programming interfaces (APIs). We performed a field study of the use of APIs and observed that they served many roles. We observed that APIs were successful indeed in supporting collaboration by serving as contracts among stakeholders as well as by reifying organizational boundaries. However, the separation that they accomplished also hindered other forms of collaboration, particularly among members of different teams. Therefore, we think argue that API's do not only have beneficial purposes. Based on our results, we discuss implications for collaborative software development tools.
Lay Summary application programming interfaces ("APIs") are a technical way of getting data out of a computer system. Recently, the United States passed legislation (the 21st Century Cures Act) requiring the...
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Lay Summary application programming interfaces ("APIs") are a technical way of getting data out of a computer system. Recently, the United States passed legislation (the 21st Century Cures Act) requiring the use of APIs for electronic health record systems, which are where most healthcare providers document clinical encounters with patients and where other clinical data is held. In this article, we asked national experts in health information technology to describe some of the ways in which APIs could be used, how they are valuable, and what some barriers may be to broader use. We found 6 main categories, or "use cases," for APIs in healthcare-patients, providers, administrative, public health, social services, and population-health. We also describe why these use cases are important, as well as barriers within each use case. As more and more health data are made available via APIs, these use cases will drive the success of these technological innovations. Objective Improving health data interoperability through application programming interfaces (APIs) is a focus of US policy initiatives and could have tremendous impact on many aspects of care delivery, such as innovation, operational efficiency, and patient-centered care. To better understand the landscape of API use cases, we interviewed US thought leaders involved in developing and implementing standard-based APIs. Materials and Methods We conducted semi-structured virtual interviews with US subject matter experts (SMEs) on APIs. SMEs were asked to describe API use cases along with value and barriers for each use case. Written summaries were checked by the SME and analyzed by the study team to identify findings and themes. Results We interviewed 12 SMEs representing diverse sectors of the US healthcare system, including academia, industry, public health agencies, electronic health record vendors, government, and standards organizations. Use cases for standards-based APIs fell into six categories: patient-facing,
Machine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy;however, their...
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Machine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy;however, their lack of transparency poses a significant challenge. Traditional ML models often fail to provide interpretable justifications for their predictions, limiting their adoption in security-critical environments where understanding the reasoning behind a detection is essential for threat mitigation and response. Explainable AI (XAI) addresses this gap by enhancing model interpretability while maintaining strong detection capabilities. This survey presents a comprehensive review of state-of-the-art ML techniques for malware analysis, with a specific focus on explainability methods and research mainly from 2018 to 2024. We examine existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable. Additionally, we explore recent advancements and highlight open research challenges in the field of explainable malware analysis. By providing a structured overview of XAI-driven malware detection approaches, this survey serves as a valuable resource for researchers and practitioners seeking to bridge the gap between ML performance and explainability in cybersecurity.
Ransomware continues to pose a significant threat to individuals and organizations worldwide, causing disruptions, financial losses, and reputational damage. As ransomware attacks grow in sophistication, understanding...
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Ransomware continues to pose a significant threat to individuals and organizations worldwide, causing disruptions, financial losses, and reputational damage. As ransomware attacks grow in sophistication, understanding their behaviour through effective analysis has become increasingly critical for mitigation and prevention. However, ransomware analysis presents several challenges. First, the sheer volume of applicationprogramming Interface (API) call data generated by ransomware during execution can overwhelm traditional analysis methods. Second, the temporal and categorical nature of this data makes identifying meaningful patterns complex. Third, the integration of machine learning (ML) models, which are essential for accurate classification, is hindered by the difficulty of modelling intricate API call behaviours. Without effective tools to address these issues, analysts risk missing critical behavioural indicators. To overcome these challenges, the proposed Ransomware Visualization (RanViz) system was developed to provide a comprehensive visual analytics and classification platform designed to enhance ransomware analysis. RanViz employs advanced visualization techniques to represent categorical API call time-series data, enabling analysts to intuitively understand ransomware behaviours that might otherwise remain obscured. The system incorporates ML models based on API call frequency, temporal interval, and sequence to classify unknown samples as either benign or ransomware. The models collectively achieve an accuracy of over 95% in detecting ransomware. By providing a unified platform that combines powerful visualization tools with high-performing ML models, RanViz simplifies ransomware analysis and offers a robust framework for accurate classification. This makes it an invaluable tool for digital forensics and cybersecurity professionals tasked with addressing the ever-evolving ransomware threat.
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