BRFplus

BRFplus (Business Rule Framework plus) is a business rule management system (BRMS) offered by SAP AG. BRFplus is part of the SAP NetWeaver ABAP stack. Therefore, all SAP applications that are based on SAP NetWeaver can access BRFplus within the boundaries of an SAP system. However, it is also possible to generate web services so that BRFplus rules can also be offered as a service in a SOA landscape, regardless of the software platform used by the service consumers. BRFplus development started as a supporting tool that was part of SAP Business ByDesign, an ERP solution targeted at small and medium size companies. By that time, the tool was called "Formula and Derivation Tool" (FDT). Later on, it was decided to maintain BRFplus on those codelines that serve as the basis for SAP Business Suite. With that, business rules that have been created for Business ByDesign can easily be taken over in a full-size SAP system where they are ready for use without any changes. == Overview == BRFplus offers a unified modeling and runtime environment for business rules that addresses both technical users (programmers, system administrators) as well as business users who take care of operational business processes (like procurement, bidding, tax form validation, etc.). The different requirements and usage scenarios of the different target groups can be covered with the help of the SAP authorization system and a user interface that can be individually customized. Being integrated into SAP NetWeaver, BRFplus-based applications can look at, and model, business rules from a strictly business-oriented perspective, rather than starting with the underlying technical artifacts. This is because the integration allows for direct access to the business objects available in the SAP dictionary (like customer, supplier, material, bill, etc.). In addition to the predefined expression types (decision table, decision tree, formula, database access, loops, etc.) and actions (sending e-mails, triggering a workflow, etc.), BRFplus can be extended by custom expression types. Also, direct calls of function modules as well as ABAP OO class methods are supported so that the entire range of the ABAP programming language is available for solving business tasks. BRFplus comes with an optional versioning mechanism. Versioning can be switched on and off for individual objects as well as for entire applications. Versioned business rules are needed in certain use cases for legal reasons, but they also allow for simulating the system behavior as it would have been at a particular point in time. Once the rule objects are in a consistent state and active, the system automatically generates ABAP OO classes that encapsulate the functional scope of the underlying rule object. This is done on an on-demand base and speeds up processing. The execution of functions as well as of single expressions can be simulated. The processing log of the simulation is useful for checking the implementation and for investigating problems. BRFplus applications can be exported and imported as an XML file. This is an easy way of creating a data backup. XML files can also be used for deploying rule applications throughout the company. == Main object types == === Application === The application object serves as a container for all the BRFplus objects that have been assembled to solve a particular business task. It is possible to define certain default settings on application level that are inherited by all objects that are created in the scope of that application. === Function === A function is used to connect a business application with the rule processing framework of BRFplus. The calling business application passes input values to the function which are then processed by the expressions and rulesets that are associated with the called function. The calculated result is then returned to the calling business application. === Expression types and action types === Boolean BRMS Connector Case Database Lookup Decision Table Decision Tree Formula Function Call Loop Procedure Call Random Number Search Tree Step Sequence Value Range1 XSL Transformation === Ruleset === A ruleset is a container for an arbitrary number of rule objects which in turn carry out the necessary calculations with the help of assigned expressions and actions. Instead of assigning an expression to a function, it is also possible to assign any number of rulesets to a function. When the function is called, all assigned rulesets are subsequently processed. === Data objects === BRFplus supports elementary data objects (text, number, boolean, time point, amount, quantity) as well as structures and tables. Structures can be nested. For all types of data objects it is possible to reference data objects that reside in the data dictionary of the backend system. With that, a BRFplus data object does not only inherit the type definition of the referenced object but can also access associated data like domain value lists or object documentation. === Other objects === With catalogs, it is possible to define business-specific subsets of the rule objects that reside in the system. This is helpful for hiding the complexity of a rule system, thus improving usability. Object filters are used by system administrators to ensure that for selected users, only a predefined subset of object types is visible. This is useful to enforce access rights as well as modeling policies. == Other BRM solutions offered by SAP == BRFplus is positioned as the successor product of an older business rule solution known as BRF (Business Rule Framework). For a longer transition phase, both solutions exist in parallel. However, an increasing number of SAP applications that used to be based on BRF are migrating to BRFplus. While BRFplus supports business rules for applications based on the SAP NetWeaver ABAP stack, SAP is offering another product named SAP NetWeaver Business Rules Management (BRM). BRM supports business rule modeling for the SAP NetWeaver Java stack. Both products do not compete. They are available in parallel and can be used in a collaborative approach to deal with use cases where both technology stacks are used in parallel. BRFplus comes with a special expression type that helps bridging the gap between the two different technologies. == Availability == BRFplus has been delivered to the public with SAP NetWeaver 7.0 Enhancement Package 1 for the first time. Being part of SAP NetWeaver, the usage of BRFplus is covered by the "SAP NetWeaver Foundation for Third Party Applications" license, with no additional costs. == Literature == Carsten Ziegler, Thomas Albrecht: BRFplus – Business Rule Management for ABAP Applications. Galileo Press 2011. ISBN 978-1-59229-293-6

Personal cloud

A personal cloud is a collection of digital content and services that are accessible from any device through the Internet. It is not a tangible entity, but a place that gives users the ability to store, synchronize, stream and share content on a relative core, moving from one platform, screen and location to another. Created on connected services and applications, it reflects and sets consumer expectations for how next-generation computing services will work. The four primary types of personal cloud in use today are: Online cloud, NAS device cloud, server device cloud, and home-made clouds. == Online cloud == The online cloud is sometimes referred to as the public cloud. It is the cloud computing model where online resources like software and data storage are made available over the Internet. Typically, an individual or organization has little control over the ecosystem in which the online cloud is hosted, and the core infrastructure is shared between many individuals and organizations. The data and applications provided by the service provider are logically segregated so that only those authorized are allowed access. == NAS device cloud == A network-attached storage (NAS) device is a computer connected to a network that provides only file-based data storage services to other devices on the network. Although it may technically be possible to run other software on a NAS device, it is not designed to be a general purpose server. Cloud NAS is remote storage that is accessed over the Internet as if it were local. A cloud NAS is often used for backups and archiving. One of the benefits of NAS Cloud is that data in the cloud can be accessed at any time from anywhere. The main drawback, however, is that the speed of the transfer rate is only as fast as the network connection the data is accessed over and can therefore be fairly slow. == Server device cloud == In many ways cloud servers work in the same way as physical servers but the functions they perform can be very different. Typically, the cloud server is an on-premises device that is connected to the Internet and gives users the functions available on the online cloud but with the added benefit and security of the files being in their control on their premises. The server cloud has been historically enterprise-based deployed by businesses needing an in-house cloud. However, there are also in-house options available for individual users. == Home-made clouds == For the more technologically proficient user a common solution for using a personal cloud is to create a home-made cloud system by connecting an external USB hard drive to a Wi-Fi router. This enables both wired and wireless computers to access the USB hard drive and use it for storage or for retrieving files a user needs to share on the network thereby acting like a cloud. Setting up a personal cloud requires a user to have particular skills in technology and network setup. One of the risks associated with improper setup is security, and leaving the files accessible to anyone with technical knowledge. Not every router supports this type of access and modification.

Manufacturing Automation Protocol

Manufacturing Automation Protocol (MAP) was a computer network standard released in 1982 for interconnection of devices from multiple manufacturers. It was developed by General Motors to combat the proliferation of incompatible communications standards used by suppliers of automation products such as programmable controllers. By 1985 demonstrations of interoperability were carried out and 21 vendors offered MAP products. In 1986 the Boeing corporation merged its Technical Office Protocol with the MAP standard, and the combined standard was referred to as "MAP/TOP". The standard was revised several times between the first issue in 1982 and MAP 3.0 in 1987, with significant technical changes that made interoperation between different revisions of the standard difficult. Although promoted and used by manufacturers such as General Motors, Boeing, and others, it lost market share to the contemporary Ethernet standard and was not widely adopted. Difficulties included changing protocol specifications, the expense of MAP interface links, and the speed penalty of a token-passing network. The token bus network protocol used by MAP became standardized as IEEE standard 802.4 but this committee disbanded in 2004 due to lack of industry attention.

Media contacts database

In public relations (PR) and marketing, a media contacts database is a resource which catalogs the names, contact information, and other details about people who work in various media professions. These include journalists, reporters, editors, publishers, contributors, freelance journalists, opinion writers, social media personalities/ influencers, TV show anchors, radio show hosts, DJs, and others. A media contacts database usually contains the following information: Full name of the media contact, The publication or channel they work for Designations (past and present) Topics they cover, or their beat Contact information found in public domains Online presence like blogs and other social networking sites Education Information == Overview == A media contacts database is a public relations tool that is maintained and used by PR professionals to pitch stories on a particular topic, product, or company to a specific group of journalists. These journalists would then write or speak about the particular topic in a relevant issue or episode of their shows. A media contacts database allows a PR professional to gain easy access to hundreds of journalists within a short span of time. Media contacts database are created and sold by many media research companies that offer such PR software for professionals.

Data thinking

Data Thinking is a framework that integrates data science with the design process. It combines computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions in product development. The framework is used to explore, design, develop, and validate solutions, with a focus on user experience and data analytics, including data collection and interpretation The framework aims to apply data literacy and inform decision-making through data-driven insights. == Major components == According to "Computational thinking in the era of data science": Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules. Data thinking evaluates whether data accurately represents real-life scenarios and improves data collection where necessary. The framework highlights the importance of preserving domain-specific meaning during data analysis. Data thinking incorporates statistical and logical analysis to identify patterns and irregularities. Data thinking involves testing solutions in real-life contexts and iteratively improving models based on new data. The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations. == Major phases == === Strategic context and risk analysis === Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like coolhunting, trend analysis, and scenario planning can be used to assist with this. === Ideation and exploration === In this phase, focus areas are identified, and use cases are developed by integrating organizational goals, user needs, and data requirements. Design thinking methods, such as personas and customer journey mapping, are applied. === Prototyping === A proof of concept is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance. === Implementation and monitoring === Solutions are tested and monitored for performance and continual improvement. == Implementing Data Thinking == The following resources explain more about data thinking and its applications: "Data Thinking: Framework for data-based solutions" by StackFuel "What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group "Data Science Thinking" by SpringerLink These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.

Adversarial stylometry

Adversarial stylometry is the practice of altering writing style to reduce the potential for stylometry to discover the author's identity or their characteristics. This task is also known as authorship obfuscation or authorship anonymisation. Stylometry poses a significant privacy challenge in its ability to unmask anonymous authors or to link pseudonyms to an author's other identities, which, for example, creates difficulties for whistleblowers, activists, and hoaxers and fraudsters. The privacy risk is expected to grow as machine learning techniques and text corpora develop. All adversarial stylometry shares the core idea of faithfully paraphrasing the source text so that the meaning is unchanged but the stylistic signals are obscured. Such a faithful paraphrase is an adversarial example for a stylometric classifier. Several broad approaches to this exist, with some overlap: imitation, substituting the author's own style for another's; translation, applying machine translation with the hope that this eliminates characteristic style in the source text; and obfuscation, deliberately modifying a text's style to make it not resemble the author's own. Manually obscuring style is possible, but laborious; in some circumstances, it is preferable or necessary. Automated tooling, either semi- or fully-automatic, could assist an author. How best to perform the task and the design of such tools is an open research question. While some approaches have been shown to be able to defeat particular stylometric analyses, particularly those that do not account for the potential of adversariality, establishing safety in the face of unknown analyses is an issue. Ensuring the faithfulness of the paraphrase is a critical challenge for automated tools. It is uncertain if the practice of adversarial stylometry is detectable in itself. Some studies have found that particular methods produced signals in the output text, but a stylometrist who is uncertain of what methods may have been used may not be able to reliably detect them. == History == Rao & Rohatgi (2000), an early work in adversarial stylometry, identified machine translation as a possibility, but noted that the quality of translators available at the time presented severe challenges. Kacmarcik & Gamon (2006) is another early work. Brennan, Afroz & Greenstadt (2012) performed the first evaluation of adversarial stylometric methods on actual texts. Brennan & Greenstadt (2009) introduced the first corpus of adversarially authored texts specifically for evaluating stylometric methods; other corpora include the International Imitation Hemingway Competition, the Faux Faulkner contest, and the hoax blog A Gay Girl in Damascus. == Motivations == Rao & Rohatgi (2000) suggest that short, unattributed documents (i.e., anonymous posts) are not at risk of stylometric identification, but pseudonymous authors who have not practiced adversarial stylometry in producing corpuses of thousands of words may be vulnerable. Narayanan et al. (2012) attempted large-scale deanonymisation of 100,000 blog authors with mixed results: the identifications were significantly better than chance, but only accurately matched the blog and author a fifth of the time; identification improved with the number of posts written by the author in the corpus. Even if an author is not identified, some of their characteristics may still be deduced stylometrically, or stylometry may narrow the anonymity set of potential authors sufficiently for other information to complete the identification. Detecting author characteristics (e.g., gender or age) is often simpler than identifying an author from a large, possibly open, set of candidates. Modern machine learning techniques offer powerful tools for identification; further development of corpora and computational stylometric techniques are likely to raise further privacy issues. Gröndahl & Asokan (2020a) say that the general validity of the hypothesis underlying stylometry—that authors have invariant, content-independent 'style fingerprints'—is uncertain, but "the deanonymisation attack is a real privacy concern". Those interested in practicing adversarial stylometry and stylistic deception include whistleblowers avoiding retribution; journalists and activists; perpetrators of frauds and hoaxes; authors of fake reviews; literary forgers; criminals disguising their identity from investigators; and, generally, anyone with a desire for anonymity or pseudonymity. Authors, or agents acting on behalf of authors, may also attempt to remove stylistic clues to author characteristics (e.g., race or gender) so that knowledge of those characteristics cannot be used for discrimination (e.g., through algorithmic bias). Another possible use for adversarial stylometry is in disguising automatically generated text as human-authored. == Methods == With imitation, the author attempts to mislead stylometry by matching their style to another author's. An incomplete imitation, where some of the true author's unique characteristics appear alongside the imitated author's, can be a detectable signal for the use of adversarial stylometry. Imitation can be performed automatically with style transfer systems, though this typically requires a large corpus in the target style for the system to learn from. Another approach is translation, which employs machine translation of a source text to eliminate characteristic style, often through multiple translators in sequence to produce a round-trip translation. Such chained translation can lead to texts being significantly altered, even to the point of incomprehensibility; improved translation tools reduce this risk. More simply-structured texts can be easier to machine translate without losing the original meaning. Machine translation blurs into direct stylistic imitation or obfuscation achieved through automated style transfer, which can be viewed as a "translation" with the same language as input and output. With low-quality translation tools, an author can be required to manually correct major translation errors while avoiding the hazard of re-introducing stylistic characteristics. Wang, Juola & Riddell (2022) found that gross errors introduced by Google Translate were rare, but more common with several intermediate translations—however, occasional simple or short sentences and misspellings in the source text appeared verbatim in the output, potentially providing an identifying signal. Chain translation can leave characteristic traces of its application in a document, which may allow reconstruction of the intermediate languages used and the number of translation steps performed. Obfuscation involves deliberately changing the style of a text to reduce its similarity to other texts by some metric; this may be performed at the time of writing by conscious modification, or as part of a revision process with feedback from the metric being targeted as an input to decide when the text has been sufficiently obfuscated. In contrast to translation, complex texts can offer more opportunities for effective obfuscation without altering meaning, and likewise genres with more permissible variation allow more obfuscation. However, longer texts are harder to thoroughly obfuscate. Obfuscation can blend into imitation if the author develops a novel target style, distinct from their original style. With respect to masking author characteristics, obfuscation may aim to achieve a union (adding signals for imitated characteristics) or an intersection (removing signals and normalising) of other authors' styles. Avoiding the author's own idiosyncrasies and producing a "normalised" text is a critical obfuscatory step: an author may have a unique tendency to misspell certain words, use particular variants, or to format a document in a characteristic way. Stylometric signals vary in how simply they can be adversarially masked; an author may easily change their vocabulary by conscious choice, but altering the pattern of grammar or the letter frequency in their text may be harder to achieve, though Juola & Vescovi (2011) report that imitation typically succeeds at masking more characteristics than obfuscation. Automated obfuscation may require large amounts of training data written by the author. Concerning automated implementations of adversarial stylometry, two possible implementations are rule-based systems for paraphrasing; and encoder–decoder architectures, where the text passes through an intermediate format that is (intended to be) style-neutral. Another division in automated methods is whether there is feedback from an identification system or not. With such feedback, finding paraphrases for author masking has been characterised as a heuristic search problem, exploring textual variants until the result is stylistically sufficiently far (in the case of obfuscation) or near (in the case of imitation), which then constitutes an adversarial example for that identification system. == Evaluation == How

Story (social media)

In social media, a story is a function in which the user tells a narrative or provides status messages and information in the form of short, time-limited clips in an automatically running sequence. == Definition == A story is a short sequence of images, videos, or other social media content, which can be accompanied by backgrounds, music, text, stickers, animations, filters or emojis. Social media platforms typically advance through the sequence automatically when presenting a story to a viewer. Although the sequential nature of stories can be used to tell a narrative, the pieces of a story can also be unrelated. Social media platforms that offer stories will typically have a primary story for each user which consists of everything the user posted to their story over a certain period of time, usually the most recent 24 hours. Most stories cannot be changed afterwards and are only available for a short time. Stories are almost exclusively created on a mobile device such as a smartphone or tablet computer and are usually displayed vertically. == History == In October 2013, Snapchat first introduced the story function as a series of Snaps that can together tell a narrative through a chronological order, with each Snap being viewable by all of the poster's friends and deleted after 24 hours. Stories soon surpassed private Snaps to become Snapchat's most-viewed type of post. After 2015, Snapchat introduced a feature allowing users to post private stories viewable by a chosen subset of their friends. Later other apps would copy this feature. In August 2016, Instagram introduced a stories function that deletes the content after 24 hours. Various commenters have accused the site of copying Snapchat. In February 2017, the instant messenger WhatsApp introduced the Now Status stories function in beta, which was later renamed Status. In March 2017, a story function was introduced in Facebook Messenger. In February 2018, Google launched AMP Stories, bringing a story-style format to certain Google search results on mobile devices. In August 2018, YouTube introduced a stories function that initially was limited to pictures, but was later expanded to support short video clips. The feature was shut down in June 2023. In August 2018, the GIF website Giphy introduced a story function. In March 2022, TikTok added a story feature which allowed users to create 15 second long videos that delete after 24 hours. In June 2023, Telegram CEO Pavel Durov announced stories for Telegram would be released in July 2023. In July 2023, the feature was released for premium users, and in August 2023 it was rolled out for all users. == User motivations == In 2022, a study performed by Jia-Dai (Evelyn) Lu and Jhih-Syuan (Elaine) Lin examined the various motivations for updating stories on Instagram. The researchers found a new configuration of motivations for using Instagram Stories: exploration, self-enhancement, perceived functionality, entertainment, social sharing, relationship building, novelty, and surveillance. The findings also highlighted that contribution and creation activities are likely to result in positive emotions, while creation alone predicts negative emotions while updating stories on Instagram. == Usage statistics == In 2019, around 1.5 billion people worldwide every day on average used the stories function in a social network or messenger. Younger people in particular use this function. More than 20% of people aged 18 to 24 use Instagram stories, while it is just under 2% of those over 55. In a Facebook survey of 18,000 participants from 12 countries, 68% said they used the stories function at least once a month. Stories in the areas of fashion and tourism are particularly popular. The website Fanpage Karma analyzed several Instagram accounts and determined the average reach of posts and stories per follower, concluding that posts have a higher reach than stories, which often have less than half the reach.