Critical data studies is the exploration of and engagement with social, cultural, and ethical challenges that arise when working with big data. It is through various unique perspectives and taking a critical approach that this form of study can be practiced. As its name implies, critical data studies draws heavily on the influence of critical theory, which has a strong focus on addressing the organization of power structures. This idea is then applied to the study of data. Interest in this unique field of critical data studies began in 2011 with scholars danah boyd and Kate Crawford posing various questions for the critical study of big data and recognizing its potential threatening impacts on society and culture. It was not until 2014, and more exploration and conversations, that critical data studies was officially coined by scholars Craig Dalton and Jim Thatcher. They put a large emphasis on understanding the context of big data in order to approach it more critically. Researchers such as David Ribes, Robert Soden, Seyram Avle, Sarah E. Fox, and Phoebe Sengers focus on understanding data as a historical artifact and taking an interdisciplinary approach towards critical data studies. Other key scholars in this discipline include Rob Kitchin and Tracey P. Lauriault who focus on reevaluating data through different spheres. Various critical frameworks that can be applied to analyze big data include Feminist, Anti-Racist, Queer, Indigenous, Decolonial, Anti-Ableist, as well as Symbolic and Synthetic data science. These frameworks help to make sense of the data by addressing power, biases, privacy, consent, and underrepresentation or misrepresentation concerns that exist in data as well as how to approach and analyze this data with a more equitable mindset. == Motivation == In their article in which they coin the term 'critical data studies,' Dalton and Thatcher also provide several justifications as to why data studies is a discipline worthy of a critical approach. First, 'big data' is an important aspect of twenty-first century society, and the analysis of 'big data' allows for a deeper understanding of what is happening and for what reasons. Big data is important to critical data studies because it is the type of data used within this field. Big data does not necessarily refer to a large data set, it can have a data set with millions of rows, but also a data set that just has a wide variety and expansive scope of data with a smaller type of dataset. As well as having whole populations in the data set and not just sample sizes. Furthermore, big data as a technological tool and the information that it yields are not neutral, according to Dalton and Thatcher, making it worthy of critical analysis in order to identify and address its biases. Building off this idea, another justification for a critical approach is that the relationship between big data and society is an important one, and therefore worthy of study. Ribes et. al. argue there is a need for an interdisciplinary understanding of data as a historical artifact as a motivating aspect of critical data studies.The overarching consensus in the Computer-Supported Cooperative Work (CSCW) field, is that people should speak for the data, and not let the data speak for itself. The sources of big data and it’s relationship to varied metadata can be a complicated one, which leads to data disorder and a need for an ethical analysis. Additionally, Iliadis and Russo (2016) have called for studying data assemblages. This is to say, data has innate technological, political, social, and economic histories that should be taken into consideration. Kitchin argues data is almost never raw, and it is almost always cooked, meaning that it is always spoken for by the data scientists utilizing it. Thus, Big Data should be open to a variety of perspectives, especially those of cultural and philosophical nature. Further, data contains hidden histories, ideologies, and philosophies. Big data technology can cause significant changes in society's structure and in the everyday lives of people, and, being a product of society, big data technology is worthy of sociological investigation. Moreover, data sets are almost never completely without any influence. Rather, data are shaped by the vision or goals of those gathering the data, and during the data collection process, certain things are quantified, stored, sorted and even discarded by the research team. A critical approach is thus necessary in order to understand and reveal the intent behind the information being presented.One of these critical approaches has been through feminist data studies. This method applies feminist principles to critical studies and data collecting and analysis. The goal of this is to address the power imbalance in data science and society. According to Catherine D’Ignazio and Lauren F. Klein, a power analysis can be performed by examining power, challenging power, evaluating emotion and embodiment, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labor visible. Feminist data studies is part of the movement towards making data to benefit everyone and not to increase existing inequalities. Moreover, data alone cannot speak for themselves; in order to possess any concrete meaning, data must be accompanied by theoretical insight or alternative quantitative or qualitative research measures. Based on different social topics such as anti-racist data studies, critical data studies give a focus on those social issues concerning data. Specifically in anti-racist data studies they use a classification approach to get representation for those within that community. Desmond Upton Patton and others used their own classification system in the communities of Chicago to help target and reduce violence with young teens on twitter. They had students in those communities help them to decipher the terminology and emojis of these teens to target the language used in tweets that followed with violence outside of the computer screens. This is just one real world example of critical data studies and its application. Dalton and Thatcher argue that if one were to only think of data in terms of its exploitative power, there is no possibility of using data for revolutionary, liberatory purposes. Finally, Dalton and Thatcher propose that a critical approach in studying data allows for 'big data' to be combined with older, 'small data,' and thus create more thorough research, opening up more opportunities, questions and topics to be explored. == Issues and concerns for critical data scholars == Data plays a pivotal role in the emerging knowledge economy, driving productivity, competitiveness, efficiency, sustainability, and capital accumulation. The ethical, political, and economic dimensions of data dynamically evolve across space and time, influenced by changing regimes, technologies, and priorities. Technically, the focus lies on handling, storing, and analyzing vast data sets, utilizing machine learning-based data mining and analytics. This technological advancement raises concerns about data quality, encompassing validity, reliability, authenticity, usability, and lineage. The use of data in modern society brings about new ways of understanding and measuring the world, but also brings with it certain concerns or issues. Data scholars attempt to bring some of these issues to light in their quest to be critical of data. Technical and organizational issues could include the scope of the data set, meaning there is too little or too much data to work with, leading to inaccurate results. It becomes crucial for critical data scholars to carefully consider the adequacy of data volume for their analyses. The quality of the data itself is another facet of concern. The data itself could be of poor quality, such as an incomplete or messy data set with missing or inaccurate data values. This would lead researchers to have to make edits and assumptions about the data itself. Addressing these issues often requires scholars to make edits and assumptions about the data to ensure its reliability and relevance. Data scientists could have improper access to the actual data set, limiting their abilities to analyze it. Linnet Taylor explains how gaps in data can arise when people of varying levels of power have certain rights to their data sources. These people in power can control what data is collected, how it is displayed and how it is analyzed. The capabilities of the research team also play a crucial role in the quality of data analytics. The research team may have inadequate skills or organizational capabilities which leads to the actual analytics performed on the dataset to be biased. This can also lead to ecological fallacies, meaning an assumption is made about an individual based on data or results from a larger group of people. These technical and organizational challenges highlight the complexity of working with data and
Security of the Java software platform
The Java software platform provides a number of features designed for improving the security of Java applications. This includes enforcing runtime constraints through the use of the Java Virtual Machine (JVM), a security manager that sandboxes untrusted code from the rest of the operating system, and a suite of security APIs that Java developers can utilise. Despite this, criticism has been directed at the programming language, and Oracle, due to an increase in malicious programs that revealed security vulnerabilities in the JVM, which were subsequently not properly addressed by Oracle in a timely manner. == Security features == === The JVM === The binary form of programs running on the Java platform is not native machine code but an intermediate bytecode. The JVM performs verification on this bytecode before running it to prevent the program from performing unsafe operations such as branching to incorrect locations, which may contain data rather than instructions. It also allows the JVM to enforce runtime constraints such as array bounds checking. This means that Java programs are significantly less likely to suffer from memory safety flaws such as buffer overflow than programs written in languages such as C which do not provide such memory safety guarantees. The platform does not allow programs to perform certain potentially unsafe operations such as pointer arithmetic or unchecked type casts. It manages memory allocation and initialization and provides automatic garbage collection which in many cases (but not all) relieves the developer from manual memory management. This contributes to type safety and memory safety. === Security manager === The platform provides a security manager which allows users to run untrusted bytecode in a "sandboxed" environment designed to protect them from malicious or poorly written software by preventing the untrusted code from accessing certain platform features and APIs. For example, untrusted code might be prevented from reading or writing files on the local filesystem, running arbitrary commands with the current user's privileges, accessing communication networks, accessing the internal private state of objects using reflection, or causing the JVM to exit. The security manager also allows Java programs to be cryptographically signed; users can choose to allow code with a valid digital signature from a trusted entity to run with full privileges in circumstances where it would otherwise be untrusted. Users can also set fine-grained access control policies for programs from different sources. For example, a user may decide that only system classes should be fully trusted, that code from certain trusted entities may be allowed to read certain specific files, and that all other code should be fully sandboxed. === Security APIs === The Java Class Library provides a number of APIs related to security, such as standard cryptographic algorithms, authentication, and secure communication protocols. === The sun.misc.Unsafe class === sun.misc.Unsafe is an internal utility class in the Java programming language which is a collection of low-level unsafe operations. While it is not a part of the official Java Class Library, it is called internally by the Java libraries. It resides in an unofficial Java module named jdk.unsupported. Beginning in Java 11, it has been partially migrated to jdk.internal.misc.Unsafe (which resides in module java.base). Its primary feature is to allow direct memory management (similar to C memory management) and memory address manipulation, manipulating objects and fields, thread manipulation, and concurrency primitives. Its declaration is: public final class Unsafe;, and it is a singleton class with a private constructor. It contains the following methods, many of which are declared native (invoking Java Native Interface): static Unsafe getUnsafe(): retrieves the Unsafe instance. It uses sun.reflect.Reflection to do so. int getInt(Object o, long offset): fetches a value (a field or array element) in the object at the given offset. (There are corresponding getBoolean(), getByte(), getShort(), getChar(), getLong(), getFloat(), and getDouble() methods as well.) void putInt(Object o, long offset, int x): stores a value into an object at the given offset. (There are corresponding putBoolean(), putByte(), putShort(), putChar(), putLong(), putFloat(), and putDouble() methods as well.) Object getObject(Object o, long offset): fetches a reference value from an object at the given offset. void putObject(Object o, long offset, Object x): stores a reference value into an object at the given offset. int getInt(long address): fetches a value at the given address. (There are corresponding getBoolean(), getByte(), getShort(), getChar(), getLong(), getFloat(), and getDouble() methods as well.) void putInt(long address, int x): stores a value into the given address. (There are corresponding putBoolean(), putByte(), putShort(), putChar(), putLong(), putFloat(), and putDouble() methods as well.) long getAddress(long address): fetches a native pointer from a given address. void putAddress(long address, long x): stores a native pointer into a given address. long allocateMemory(long bytes): allocates a block of native memory of the given size (similar to malloc()). long reallocateMemory(long address, long bytes): resizes a block of native memory to the given size (similar to realloc()). void setMemory(Object o, long offset, long bytes, byte value), void setMemory(long address, long bytes, byte value): sets all bytes in a block of memory to a fixed value (similar to memset()). void copyMemory(Object srcBase, long srcOffset, Object destBase, long destOffset, long bytes), void copyMemory(long srcAddress, long destAddress, long bytes): sets all bytes in a given block of memory to a copy of another block (similar to memcpy()). void freeMemory(long address): deallocates a block of native memory obtained from allocateMemory() or reallocateMemory(), similar to free()). long staticFieldOffset(Field f): obtains the location of a given field in the storage allocation of its class. long objectFieldOffset(Field f): obtains the location of a given static field in conjunction with staticFieldBase(). Object staticFieldBase(Field f): obtains the location of a given static field in conjunction with staticFieldOffset(). void ensureClassInitialized(Class c): ensures the given class has been initialized. int arrayBaseOffset(Class arrayClass): obtains the offset of the first element in the storage allocation of a given array class. int arrayIndexScale(Class arrayClass): obtains the scale factor for addressing elements in the storage allocation of a given array class. static int addressSize(): obtains the size (in bytes) of a native pointer. int pageSize(): obtains the size (in bytes) of a native memory page. Class defineClass(String name, byte[] b, int off, int len, ClassLoader loader, ProtectionDomain protectionDomain): signals to the JVM to define a class without security checks. Class defineAnonymousClass(Class hostClass, byte[] data, Object[] cpPatches): signals to the JVM to define a class but do not make it known to the class loader or system directory. Object allocateInstance(Class cls) throws InstantiationException: allocates an instance of a class without running its constructor. void monitorEnter(Object o): locks an object. void monitorExit(Object o): unlocks an object. boolean tryMonitorEnter(Object o): tries to lock an object, returning whether the lock succeeded. void throwException(Throwable ee): throws an exception without telling the verifier. final boolean compareAndSwapInt(Object o, long offset, int expected, int x): updates a variable to x if it is holding expected, returning whether the operation succeeded. (There are corresponding compareAndSwapLong() and compareAndSwapObject() methods as well.) int getIntVolatile(Object o, long offset): volatile version of getInt(). (There are corresponding getBooleanVolatile(), getByteVolatile(), getShortVolatile(), getCharVolatile(), getLongVolatile(), getFloatVolatile(), getDoubleVolatile(), and getObjectVolatile() methods as well.) void putIntVolatile(Object o, long offset, int x): volatile version of putInt(). (There are corresponding putBooleanVolatile(), putByteVolatile(), putShortVolatile(), putCharVolatile(), putLongVolatile(), putFloatVolatile(), putDoubleVolatile(), and putObjectVolatile() methods as well.) void putOrderedInt(Object o, long offset, int x): version of putIntVolatile() not guaranteeing immediate visibility of storage to other threads. (There are corresponding putOrderedLong() and putOrderedObject() methods as well.) void unpark(Object thread): unblocks a thread. void park(boolean isAbsolute, long time): blocks the current thread. int getLoadAverage(double[] loadavg, int nelems): gets the load average in the system run queue assigned to available processors averaged over various periods of time. void invokeCleaner(ByteBuffe
Algorithm characterizations
Algorithm characterizations are attempts to formalize the word algorithm. Algorithm does not have a generally accepted formal definition. Researchers are actively working on this problem. This article will present some of the "characterizations" of the notion of "algorithm" in more detail. == The problem of definition == Over the last 200 years, the definition of the algorithm has become more complicated and detailed as researchers have tried to pin down the term. Indeed, there may be more than one type of "algorithm". But most agree that algorithm has something to do with defining generalized processes for the creation of "output" integers from other "input" integers – "input parameters" arbitrary and infinite in extent, or limited in extent but still variable—by the manipulation of distinguishable symbols (counting numbers) with finite collections of rules that a person can perform with paper and pencil. The most common number-manipulation schemes—both in formal mathematics and in routine life—are: (1) the recursive functions calculated by a person with paper and pencil, and (2) the Turing machine or its Turing equivalents—the primitive register-machine or "counter-machine" model, the random-access machine model (RAM), the random-access stored-program machine model (RASP) and its functional equivalent "the computer". When we are doing "arithmetic" we are really calculating by the use of "recursive functions" in the shorthand algorithms we learned in grade school, for example, adding and subtracting. The proofs that every "recursive function" we can calculate by hand we can compute by machine and vice versa—note the usage of the words calculate versus compute—is remarkable. But this equivalence together with the thesis (unproven assertion) that this includes every calculation/computation indicates why so much emphasis has been placed upon the use of Turing-equivalent machines in the definition of specific algorithms, and why the definition of "algorithm" itself often refers back to "the Turing machine". This is discussed in more detail under Stephen Kleene's characterization. The following are summaries of the more famous characterizations (Kleene, Markov, Knuth) together with those that introduce novel elements—elements that further expand the definition or contribute to a more precise definition. [ A mathematical problem and its result can be considered as two points in a space, and the solution consists of a sequence of steps or a path linking them. Quality of the solution is a function of the path. There might be more than one attribute defined for the path, e.g. length, complexity of shape, an ease of generalizing, difficulty, and so on. ] == Chomsky hierarchy == There is more consensus on the "characterization" of the notion of "simple algorithm". All algorithms need to be specified in a formal language, and the "simplicity notion" arises from the simplicity of the language. The Chomsky (1956) hierarchy is a containment hierarchy of classes of formal grammars that generate formal languages. It is used for classifying of programming languages and abstract machines. From the Chomsky hierarchy perspective, if the algorithm can be specified on a simpler language (than unrestricted), it can be characterized by this kind of language, else it is a typical "unrestricted algorithm". Examples: a "general purpose" macro language, like M4 is unrestricted (Turing complete), but the C preprocessor macro language is not, so any algorithm expressed in C preprocessor is a "simple algorithm". See also Relationships between complexity classes. == Features of a good algorithm == The following are desirable features of a well-defined algorithm, as discussed in Scheider and Gersting (1995): Unambiguous Operations: an algorithm must have specific, outlined steps. The steps should be exact enough to precisely specify what to do at each step. Well-Ordered: The exact order of operations performed in an algorithm should be concretely defined. Feasibility: All steps of an algorithm should be possible (also known as effectively computable). Input: an algorithm should be able to accept a well-defined set of inputs. Output: an algorithm should produce some result as an output, so that its correctness can be reasoned about. Finiteness: an algorithm should terminate after a finite number of instructions. Properties of specific algorithms that may be desirable include space and time efficiency, generality (i.e. being able to handle many inputs), or determinism. == 1881 John Venn's negative reaction to W. Stanley Jevons's Logical Machine of 1870 == In early 1870 W. Stanley Jevons presented a "Logical Machine" (Jevons 1880:200) for analyzing a syllogism or other logical form e.g. an argument reduced to a Boolean equation. By means of what Couturat (1914) called a "sort of logical piano [,] ... the equalities which represent the premises ... are "played" on a keyboard like that of a typewriter. ... When all the premises have been "played", the panel shows only those constituents whose sum is equal to 1, that is, ... its logical whole. This mechanical method has the advantage over VENN's geometrical method..." (Couturat 1914:75). For his part John Venn, a logician contemporary to Jevons, was less than thrilled, opining that "it does not seem to me that any contrivances at present known or likely to be discovered really deserve the name of logical machines" (italics added, Venn 1881:120). But of historical use to the developing notion of "algorithm" is his explanation for his negative reaction with respect to a machine that "may subserve a really valuable purpose by enabling us to avoid otherwise inevitable labor": (1) "There is, first, the statement of our data in accurate logical language", (2) "Then secondly, we have to throw these statements into a form fit for the engine to work with – in this case the reduction of each proposition to its elementary denials", (3) "Thirdly, there is the combination or further treatment of our premises after such reduction," (4) "Finally, the results have to be interpreted or read off. This last generally gives rise to much opening for skill and sagacity." He concludes that "I cannot see that any machine can hope to help us except in the third of these steps; so that it seems very doubtful whether any thing of this sort really deserves the name of a logical engine."(Venn 1881:119–121). == 1943, 1952 Stephen Kleene's characterization == This section is longer and more detailed than the others because of its importance to the topic: Kleene was the first to propose that all calculations/computations—of every sort, the totality of—can equivalently be (i) calculated by use of five "primitive recursive operators" plus one special operator called the mu-operator, or be (ii) computed by the actions of a Turing machine or an equivalent model. Furthermore, he opined that either of these would stand as a definition of algorithm. A reader first confronting the words that follow may well be confused, so a brief explanation is in order. Calculation means done by hand, computation means done by Turing machine (or equivalent). (Sometimes an author slips and interchanges the words). A "function" can be thought of as an "input-output box" into which a person puts natural numbers called "arguments" or "parameters" (but only the counting numbers including 0—the nonnegative integers) and gets out a single nonnegative integer (conventionally called "the answer"). Think of the "function-box" as a little man either calculating by hand using "general recursion" or computing by Turing machine (or an equivalent machine). "Effectively calculable/computable" is more generic and means "calculable/computable by some procedure, method, technique ... whatever...". "General recursive" was Kleene's way of writing what today is called just "recursion"; however, "primitive recursion"—calculation by use of the five recursive operators—is a lesser form of recursion that lacks access to the sixth, additional, mu-operator that is needed only in rare instances. Thus most of life goes on requiring only the "primitive recursive functions." === 1943 "Thesis I", 1952 "Church's Thesis" === In 1943 Kleene proposed what has come to be known as Church's thesis: "Thesis I. Every effectively calculable function (effectively decidable predicate) is general recursive" (First stated by Kleene in 1943 (reprinted page 274 in Davis, ed. The Undecidable; appears also verbatim in Kleene (1952) p.300) In a nutshell: to calculate any function the only operations a person needs (technically, formally) are the 6 primitive operators of "general" recursion (nowadays called the operators of the mu recursive functions). Kleene's first statement of this was under the section title "12. Algorithmic theories". He would later amplify it in his text (1952) as follows: "Thesis I and its converse provide the exact definition of the notion of a calculation (decision) procedure or algorithm, for the
ArchiMate
ArchiMate ( AR-ki-mayt) is an open and independent enterprise architecture modeling language to support the description, analysis and visualization of architecture within and across business domains in an unambiguous way. ArchiMate is a technical standard from The Open Group and is based on concepts from the now superseded IEEE 1471 standard. It is supported by various tool vendors and consulting firms. ArchiMate is also a registered trademark of The Open Group. The Open Group has a certification program for ArchiMate users, software tools and courses. ArchiMate distinguishes itself from other languages such as Unified Modeling Language (UML) and Business Process Modeling and Notation (BPMN) by its enterprise modelling scope. Also, UML and BPMN are meant for a specific use and they are quite heavy – containing about 150 (UML) and 250 (BPMN) modeling concepts whereas ArchiMate works with just about 50 (in version 2.0). The goal of ArchiMate is to be ”as small as possible”, not to cover every edge scenario imaginable. To be easy to learn and apply, ArchiMate was intentionally restricted “to the concepts that suffice for modeling the proverbial 80% of practical cases". == Overview == ArchiMate offers a common language for describing the construction and operation of business processes, organizational structures, information flows, IT systems, and technical infrastructure. This insight helps the different stakeholders to design, assess, and communicate the consequences of decisions and changes within and between these business domains. The main concepts and relationships of the ArchiMate language can be seen as a framework, the so-called Archimate Framework: It divides the enterprise architecture into a business, application and technology layer. In each layer, three aspects are considered: active elements, an internal structure and elements that define use or communicate information. One of the objectives of the ArchiMate language is to define the relationships between concepts in different architecture domains. The concepts of this language therefore hold the middle between the detailed concepts, which are used for modeling individual domains (for example, the Unified Modeling Language (UML) for modeling software products), and Business Process Model and Notation (BPMN), which is used for business process modeling. == History == ArchiMate is partly based on the now superseded IEEE 1471 standard. It was developed in the Netherlands by a project team from the Telematica Instituut in cooperation with several Dutch partners from government, industry and academia. Among the partners were Ordina NV, Radboud Universiteit Nijmegen, the Leiden Institute for Advanced Computer Science (LIACS) and the Centrum Wiskunde & Informatica (CWI). Later, tests were performed in organizations such as ABN AMRO, the Dutch Tax and Customs Administration and the ABP. The development process lasted from July 2002 to December 2004, and took about 35 person years and approximately 4 million euros. The development was funded by the Dutch government (Dutch Tax and Customs Administration), and business partners, including ABN AMRO and the ABP Pension Fund. In 2008 the ownership and stewardship of ArchiMate was transferred to The Open Group. It is now managed by the ArchiMate Forum within The Open Group. In February 2009 The Open Group published the ArchiMate 1.0 standard as a formal technical standard. In January 2012 the ArchiMate 2.0 standard, and in 2013 the ArchiMate 2.1 standard was released. In June 2016, the Open Group released version 3.0 of the ArchiMate Specification. An update to Archimate 3.0.1 came out in August 2017. Archimate 3.1 was published 5 November 2019. The latest version of the ArchiMate Specification is version 3.2 released October 2022. Version 3.0 adds enhanced support for capability-oriented strategic modelling, new entities representing physical resources (for modelling the ingredients, equipment and transport resources used in the physical world) and a generic metamodel showing the entity types and the relationships between them. == ArchiMate framework == === Core framework === The main concepts and elements of the ArchiMate language are being presented as ArchiMate core framework. It consists of three layers and three aspects. This creates a matrix of combinations. Every layer has its passive structure, behavior and active structure aspects. ==== Layers ==== ArchiMate has a layered and service-oriented look on architectural models. The higher layers make use of services that are provided by the lower layers. Although, at an abstract level, the concepts that are used within each layer are similar, we define more concrete concepts that are specific for a certain layer. In this context, we distinguish three main layers: The business layer is about business processes, services, functions and events of business units. This layer "offers products and services to external customers, which are realized in the organization by business processes performed by business actors and roles". The application layer is about software applications that "support the components in the business with application services". The technology layer deals "with the hardware and communication infrastructure to support the application layer. This layer offers infrastructural services needed to run applications, realized by computer and communication hardware and system software". Each of these main layers can be further divided in sub-layers. For example, in the business layer, the primary business processes realising the products of a company may make use of a layer of secondary (supporting) business processes; in the application layer, the end-user applications may make use of generic services offered by supporting applications. On top of the business layer, a separate environment layer may be added, modelling the external customers that make use of the services of the organisation (although these may also be considered part of the business layer). In line with service orientation, the most important relation between layers is formed by use relations, which show how the higher layers make use of the services of lower layers. However, a second type of link is formed by realisation relations: elements in lower layers may realise comparable elements in higher layers; e.g., a ‘data object’ (application layer) may realise a ‘business object’ (business layer); or an ‘artifact’ (technology layer) may realise either a ‘data object’ or an ‘application component’ (application layer). ==== Aspects ==== Passive structure is the set of entities on which actions are conducted. In the business layer the example would be information objects, in the application layer data objects and in the technology layer, they could include physical objects. Behavior refers to the processes and functions performed by the actors. "Structural elements are assigned to behavioral elements, to show who or what displays the behavior". Active structure is the set of entities that display some behavior, e.g. business actors, devices, or application components. === Full framework === The Full ArchiMate framework is enriched by the physical layer, which was added to allow modeling of “physical equipment, materials, and distribution networks” and was not present in the previous version. The implementation and migration layer adds elements that allow architects to model a state of transition, to mark parts of the architecture that are temporary for the purpose, as the name says, of implementation and migration. Strategy layer adds three elements: resource, capability and course of action. These elements help to incorporate strategic dimension to the ArchiMate language by allowing it to depict the usage of resources and capabilities in order to achieve some strategic goals. Finally, there is a motivation aspect that allows different stakeholders to describe the motivation of specific actors or domains, which can be quite important when looking at one thing from several different angles. It adds several elements like stakeholder, value, driver, goal, meaning etc. == ArchiMate language == The ArchiMate language is formed as a top-level and is hierarchical. On the top, there is a model. A model is a collection of concepts. A concept can be either an element or a relationship. An element can be either of behavior type, structure, motivation or a so-called composite element (which means that it does not fit just one aspect of the framework, but two or more). The functionality of all concepts without a dependency on a specific layer is described by the generic metamodel. This layer-independent description of concepts is useful when trying to understand the mechanics of the Archimate language. === Concepts === ==== Elements ==== The generic elements are distributed into the same categories as the layers: Active structure elements Behavior elements Passive structure elements Motivation elements Active structure e
Informationist
An informationist (or information specialist in context) provides research and knowledge management services in the context of clinical care or biomedical research. Although there is no one educational pathway or formalized set of skills or knowledge for informationists, one way to think of the informationist is as one who possesses the knowledge and skill of a medical librarian with extensive research specialization and some formal clinical or public health education that goes beyond on-the-job osmosis. Medical librarians and other biomedical professional organizations have been exploring the possibilities for evaluating how informationists are being used and whether their activities supplement or replace medical library activity. More generally, an informationist is a professional who works with information within a particular business, analytic or scientific context to drive toward outcomes based on evidence, analysis, prediction and execution. For example, an extension of the term is increasingly emerging in financial services, life sciences and health care industries. Though still nascently in use, its adoption applies to individuals with extensive industry expertise, acute familiarity with organizational structures and processes, deep domain level information mastery and information systems technical savvy. Informationists in this context support transformational initiatives within and across functional areas of an enterprise as architects, governance experts, continuous improvement advocates and strategists. == Background == The term was proposed in 2000 by Davidoff & Florance. Their editorial suggested that physicians should be delegating their information needs to informationists, just as they currently order CT scans from radiologists or cardiac catheterizations from cardiologists. They conceived of an information professional who was embedded in (and indeed, supported by) the clinical departments. Supporters of the concept see it as a means for librarians to reinvigorate connections with the faculty/clinicians, as well as provide superior service by dint of informationists' biomedical training. Critics complained that the idea is nothing new; librarians already provide in-depth, high quality information services and clinical medical librarians have been working alongside physicians, nurses and other clinicians for years. Large informationist programs in the U.S. exist at the National Institutes of Health and at Vanderbilt University. Welch Medical Library at Johns Hopkins University (JHU) is developing an informationist service model in which its 10 clinical and public health librarians are moving from serving as liaison librarians for assigned departments toward becoming embedded informationists within their departments. To prepare for the embedded informationist role, librarians are undertaking education as needed to supplement their backgrounds. For example, librarians bring experience in clinical behavior counseling, public health, nursing, and more. Informationist training can then focus upon filling gaps in research methods knowledge more so than on gaining additional knowledge in the librarian's area of expertise. Courses, seminars and workshops being undertaken include those covering systematic reviews, evidence-based medicine, critical appraisal, medical language, anatomy and physiology, biostatistics, and clinical research. The term informationist is related to that of informatician—also informaticist—and many informationists do possess skills in clinical topics, bioinformatics, and biomedical informatics. Harvard University, the University of Pittsburgh, and Washington University in St. Louis are examples of institutional libraries which have hired PhD-level scientists (who may or may not have library degrees) to provide informatics support for biomedical research.
Powerset (company)
Powerset was an American company based in San Francisco, California, that, in 2006, was developing a natural language search engine for the Internet. On July 1, 2008, Powerset was acquired by Microsoft for an estimated $100 million (~$143 million in 2024). Powerset was working on building a natural language search engine that could find targeted answers to user questions (as opposed to keyword based search). For example, when confronted with a question like "Which U.S. state has the highest income tax?", conventional search engines ignore the question phrasing and instead do a search on the keywords "state", "highest", "income", and "tax". Powerset on the other hand, attempts to use natural language processing to understand the nature of the question and return pages containing the answer. The company was in the process of "building a natural language search engine that reads and understands every sentence on the Web". The company has licensed natural language technology from PARC, the former Xerox Palo Alto Research Center. On May 11, 2008, the company unveiled a tool for searching a fixed subset of English Wikipedia using conversational phrases rather than keywords. Acquisition by Microsoft: One significant milestone in Powerset's history was its acquisition by Microsoft on July 1, 2008, for an estimated $100 million. This acquisition was part of Microsoft's broader strategy to enhance its search capabilities and compete more effectively with other search engine providers, particularly Google. Natural Language Search Engine: Powerset's primary focus was on developing a natural language search engine capable of understanding and interpreting user queries in a more human-like manner. Instead of simply matching keywords, Powerset aimed to comprehend the meaning behind the words, allowing for more accurate and contextually relevant search results. Technology and Partnerships: Powerset had licensed natural language technology from PARC, the Xerox Palo Alto Research Center. This technology likely played a crucial role in the development of Powerset's NLP capabilities. Wikipedia Search Tool: In May 2008, Powerset unveiled a search tool that allowed users to search a fixed subset of English Wikipedia using conversational phrases rather than traditional keywords. This demonstrated the potential of Powerset's NLP technology in providing more precise and relevant search results. == Powerlabs == In a form of beta testing, Powerset opened an online community called Powerlabs on September 17, 2007. Business Week said: "The company hopes the site will marshal thousands of people to help build and improve its search engine before it goes public next year." Said The New York Times: "[Powerset Labs] goes far beyond the 'alpha' or 'beta' testing involved in most software projects, when users put a new product through rigorous testing to find its flaws. Powerset doesn’t have a product yet, but rather a collection of promising natural language technologies, which are the fruit of years of research at Xerox PARC." Powerlabs' initial search results are taken from Wikipedia. == Notable people == Barney Pell (born March 18, 1968, in Hollywood, California) was co-founder and CEO of Powerset. Pell received his Bachelor of Science degree in symbolic systems from Stanford University in 1989, where he graduated Phi Beta Kappa and was a National Merit Scholar. Pell received a PhD in computer science from Cambridge University in 1993, where he was a Marshall Scholar. He has worked at NASA, as chief strategist and vice president of business development at StockMaster.com (acquired by Red Herring in March, 2000) and at Whizbang! Labs. Prior to joining Powerset, Pell was an Entrepreneur-in-Residence at Mayfield Fund, a venture capital firm in Silicon Valley. Pell is also a founder of Moon Express, Inc., a U.S. company awarded a $10M commercial lunar contract by NASA and a competitor in the Google Lunar X PRIZE. Steve Newcomb was the COO and co-founder of Powerset. Prior to joining Powerset, he was a co-founder of Loudfire, General Manager at Promptu, and was on the board of directors at Jaxtr. He left Powerset in October 2007 to form Virgance, a social startup incubator. Lorenzo Thione (born in Como, Italy) was the product architect and co-founder of Powerset. Prior to joining Powerset, he worked at FXPAL in natural language processing and related research fields. Thione earned his master's degree in software engineering from the University of Texas at Austin. Ronald Kaplan, former manager of research in Natural Language Theory and Technology at PARC, served as the company's CTO and CSO. Ryan Ferrier is a member of the founding team of Powerset. He managed personnel and internal operations. After 2008 he went on to co-found Serious Business, which made Facebook applications and was later bought by Zynga. Another Powerset alumnus, Alex Le, became CTO of Serious Business and went on to become an executive producer at Zynga when it bought the company. Siqi Chen founded a stealth startup in mobile computing after leaving Powerset. Tom Preston-Werner worked at Powerset and left after the acquisition to found GitHub. == Investors == Powerset attracted a wide range of investors, many of whom had considerable experience in the venture capital field. The company received $12.5 million (~$18.2 million in 2024) in Series A funding during November 2007, co-led by the venture capital firms Foundation Capital and The Founders Fund. Among the better-known investors: Esther Dyson, founding chairman of ICANN, founder of the newsletter Release 1.0 and editor at Cnet Peter Thiel, founder and former CEO of PayPal Luke Nosek, founder of PayPal Todd Parker. Managing Partner, Hidden River Ventures Reid Hoffman, executive vice president of PayPal and founder of LinkedIn First Round Capital, seed-stage venture firm
Distributed transaction
A distributed transaction operates within a distributed environment, typically involving multiple nodes across a network depending on the location of the data. A key aspect of distributed transactions is atomicity, which ensures that the transaction is completed in its entirety or not executed at all. It's essential to note that distributed transactions are not limited to databases. The Open Group, a vendor consortium, proposed the X/Open Distributed Transaction Processing Model (X/Open XA), which became a de facto standard for the behavior of transaction model components. Databases are common transactional resources and, often, transactions span a couple of such databases. In this case, a distributed transaction can be seen as a database transaction that must be synchronized (or provide ACID properties) among multiple participating databases which are distributed among different physical locations. The isolation property (the I of ACID) poses a special challenge for multi database transactions, since the (global) serializability property could be violated, even if each database provides it (see also global serializability). In practice most commercial database systems use strong strict two-phase locking (SS2PL) for concurrency control, which ensures global serializability, if all the participating databases employ it. A common algorithm for ensuring correct completion of a distributed transaction is the two-phase commit (2PC). This algorithm is usually applied for updates able to commit in a short period of time, ranging from couple of milliseconds to couple of minutes. There are also long-lived distributed transactions, for example a transaction to book a trip, which consists of booking a flight, a rental car and a hotel. Since booking the flight might take up to a day to get a confirmation, two-phase commit is not applicable here, it will lock the resources for this long. In this case more sophisticated techniques that involve multiple undo levels are used. The way you can undo the hotel booking by calling a desk and cancelling the reservation, a system can be designed to undo certain operations (unless they are irreversibly finished). In practice, long-lived distributed transactions are implemented in systems based on web services. Usually these transactions utilize principles of compensating transactions, Optimism and Isolation Without Locking. The X/Open standard does not cover long-lived distributed transactions. Several technologies, including Jakarta Enterprise Beans and Microsoft Transaction Server fully support distributed transaction standards. == Synchronization == In event-driven architectures, distributed transactions can be synchronized through using request–response paradigm and it can be implemented in two ways: Creating two separate queues: one for requests and the other for replies. The event producer must wait until it receives the response. Creating one dedicated ephemeral queue for each request.