Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information to inform business strategies and business operations. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. BI tools can handle large amounts of structured and sometimes unstructured data to help organizations identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights is assumed to potentially provide businesses with a competitive market advantage and long-term stability, and help them take strategic decisions. Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, business intelligence is considered most effective when it combines data from the market in which a company operates (external data) with data from internal company sources, such as financial and operational information. When integrated, external and internal data provide a comprehensive view that creates ‘intelligence’ not possible from any single data source alone. Among their many uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts. BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as "BI/DW" or as "BIDW". A data warehouse contains a copy of analytical data that facilitates decision support. == History == The earliest known use of the term business intelligence is in Richard Millar Devens' Cyclopædia of Commercial and Business Anecdotes (1865). Devens used the term to describe how the banker Sir Henry Furnese gained profit by receiving and acting upon information about his environment, prior to his competitors: Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news. The ability to collect and react accordingly based on the information retrieved, Devens says, is central to business intelligence. When Hans Peter Luhn, a researcher at IBM, used the term business intelligence in an article published in 1958, he employed the Webster's Dictionary definition of intelligence: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal." In 1989, Howard Dresner (later a Gartner analyst) proposed business intelligence as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems." It was not until the late 1990s that this usage was widespread. == Definition == According to Solomon Negash and Paul Gray, business intelligence (BI) can be defined as systems that combine: Data gathering Data storage Knowledge management with analysis to evaluate complex corporate and competitive information for presentation to planners and decision makers, with the objective of improving the timeliness and the quality of the input to the decision process." According to Forrester Research, business intelligence is "a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making." Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.). Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack. Some elements of business intelligence are: Multidimensional aggregation and allocation Denormalization, tagging, and standardization Realtime reporting with analytical alert A method of interfacing with unstructured data sources Group consolidation, budgeting, and rolling forecasts Statistical inference and probabilistic simulation Key performance indicators optimization Version control and process management Open item management Forrester distinguishes this from the business-intelligence market, which is "just the top layers of the BI architectural stack, such as reporting, analytics, and dashboards." === Compared with competitive intelligence === Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes, and disseminates information with a topical focus on company competitors. If understood broadly, competitive intelligence can be considered as a subset of business intelligence. === Compared with business analytics === Business intelligence and business analytics are sometimes used interchangeably, but there are alternate definitions. Thomas Davenport, professor of information technology and management at Babson College argues that business intelligence should be divided into querying, reporting, Online analytical processing (OLAP), an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI focusing on statistics, prediction, and optimization, rather than the reporting functionality. == Unstructured data == Business operations can generate a very large amount of data in the form of emails, memos, notes from call centers, news, user groups, chats, reports, web pages, presentations, image files, video files, and marketing material. According to Merrill Lynch, more than 85% of all business information exists in these forms; a company might only use such a document a single time. Because of the way it is produced and stored, this information is either unstructured or semi-structured. The management of semi-structured data is an unsolved problem in the information technology industry. According to projections from Gartner (2003), white-collar workers spend 30–40% of their time searching, finding, and assessing unstructured data. BI uses both structured and unstructured data. The former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision-making. Because of the difficulty of properly searching, finding, and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task, or project. This can ultimately lead to poorly informed decision-making. Therefore, when designing a business intelligence/DW solution, the specific problems associated with semi-structured and unstructured data must be accommodated, as well as those associated with structured data. === Limitations of semi-structured and unstructured data === There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are: Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats. Terminology – Among researchers and analysts, there is a need to develop standardized terminology. Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis. Searchability of unstructured textual data – A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008) gives an example: "a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies". === Metadata === To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata. Many systems already capture some metadata (e.g. filename, author, size, etc.), but more usef
Interlacing (bitmaps)
In computing, interlacing (also known as interleaving) is a method of encoding a bitmap image such that a person who has partially received it sees a degraded copy of the entire image. When communicating over a slow communications link, this is often preferable to seeing a perfectly clear copy of one part of the image, as it helps the viewer decide more quickly whether to abort or continue the transmission. Interlacing is supported by the following formats, where it is optional: GIF interlacing stores the lines in the order 0 , 8 , 16 , … , ( 8 n ) , 4 , 12 , … , ( 8 n + 4 ) , 2 , 6 , 10 , 14 , … , ( 4 n + 2 ) , 1 , 3 , 5 , 7 , 9 , … , ( 2 n + 1 ) . {\displaystyle 0,8,16,\dots ,(8n),\ 4,12,\dots ,(8n+4),\ 2,6,10,14,\dots ,(4n+2),\ 1,3,5,7,9,\dots ,(2n+1).} PNG uses the Adam7 algorithm, which interlaces in both the vertical and horizontal direction. TGA uses two optional interlacing algorithms: Two-way: 0 , 2 , 4 , … , ( 2 n ) , 1 , 3 , … , ( 2 n + 1 ) , {\displaystyle 0,2,4,\dots ,(2n),\ 1,3,\dots ,(2n+1),} And four-way: 0 , 4 , 8 , … , ( 4 n ) , 1 , 5 , … , ( 4 n + 1 ) , 2 , 6 , … , ( 4 n + 2 ) , 3 , 7 , … , ( 4 n + 3 ) . {\displaystyle 0,4,8,\dots ,(4n),\ 1,5,\dots ,(4n+1),\ 2,6,\dots ,\ (4n+2),3,7,\dots ,(4n+3).} JPEG, JPEG 2000, and JPEG XR (actually using a frequency decomposition hierarchy rather than interlacing of pixel values) PGF (also using a frequency decomposition) Interlacing is a form of incremental decoding, because the image can be loaded incrementally. Another form of incremental decoding is progressive scan. In progressive scan the loaded image is decoded line for line, so instead of becoming incrementally clearer it becomes incrementally larger. The main difference between the interlace concept in bitmaps and in video is that even progressive bitmaps can be loaded over multiple frames. For example: Interlaced GIF is a GIF image that seems to arrive on your display like an image coming through a slowly opening Venetian blind. A fuzzy outline of an image is gradually replaced by seven successive waves of bit streams that fill in the missing lines until the image arrives at its full resolution. Interlaced graphics were once widely used in web design and before that in the distribution of graphics files over bulletin board systems and other low-speed communications methods. The practice is much less common today, as common broadband internet connections allow most images to be downloaded to the user's screen nearly instantaneously, and interlacing is usually an inefficient method of encoding images. Interlacing has been criticized because it may not be clear to viewers when the image has finished rendering, unlike non-interlaced rendering, where progress is apparent (remaining data appears as blank). Also, the benefits of interlacing to those on low-speed connections may be outweighed by having to download a larger file, as interlaced images typically do not compress as well.
Errored second
In telecommunications and data communication systems, an errored second is an interval of a second during which any error whatsoever has occurred, regardless of whether that error was a single bit error or a complete loss of communication for that entire second. The type of error is not important for the purpose of counting errored seconds. In communication systems with very low uncorrected bit error rates, such as modern fiber-optic transmission systems, or systems with higher low-level error rates that are corrected using large amounts of forward error correction, errored seconds are often a better measure of the effective user-visible error rate than the raw bit error rate. For many modern packet-switched communication systems, even a single uncorrected bit error is enough to cause the loss of a data packet by causing its CRC check to fail; whether that packet loss was caused by a single bit error or a hundred-bit-long error burst is irrelevant. For systems using large amounts of forward error correction, the reverse applies; a single low-level bit error will almost never occur, since any small errors will almost always be corrected, but any error sufficiently large to cause the forward error correction to fail will almost always result in a large burst error. More specialist and precise definitions of errored seconds exist in standards such as the T1 and DS1 transport systems.
Digital first
Digital first is a communication theory that publishers should release content into new media channels in preference to old media. The premise behind the theory is that after the advent of Internet, most established media organizations continued to give priority to traditional media. Over time, those organizations faced a choice to either publish first in digital media or traditional media. A "digital first" decision occurs when a publisher chooses to distribute information online in preference to or at the expense of traditional media like print publishing. Many employers and employees find it challenging to imagine using digital first practices. Distributing content digital first introduces new practices, including a need to manage the data which tracks readership. Many paper print publishers feel intimidated by the idea of publishing content online before publishing it in paper media. Comedian John Oliver in the show Last Week Tonight criticized digital first practices as a cause of lower standards in journalism. == Digital-First Transformation in Business and Education == The classical perspective of an information system is that it represents and reflects physical reality. However, it is increasingly evident that digital technologies not only represent reality but also actively shape it, as, in many instances, the digital version is created first, and the physical version follows. Gradually, digital infrastructures are integrated in people's work and life, shaping a digital environment through technologies such as 5G, sensors, and blockchain. The Digital First Framework, developed by Professor Youngjin Yoo, is a conceptual approach that helps the physical companies in the integration of digital technologies into the core of product and service design. The shift from traditional cars, where the physical vehicle precedes its digital representation on Google maps, to autonomous vehicles, where the digital representation (the blue dot) is created first, emphasizes the digital-first mindset in the design and operation of systems. In today's business environment, it's critical for organizations to embrace a digital-first strategy. Companies built on digital platforms will significantly diverge from traditional, hierarchical business structures that typically focus on a single product or market. These digitally-centered enterprises will offer products and services that are tailored to individual requirements, utilizing algorithms to assess needs based on specific situations, and relying on external partners to provide these solutions. This highlights the need to transform traditional R&D practices. It's essential for R&D teams to move beyond their laboratories and immerse themselves in the environments of their users. Understanding the context of use is fundamental to creating a relevant platform. As an illustration, the concept of Digital-first, as defined by Rohm et al. (2019), involves the integration of digital projects within educational courses, exemplified by institutions like M-School. The program adopts a programmatic approach, where successive courses progressively build upon one another, adopting an all-encompassing perspective that regards all aspects of marketing as inherently digital. Students actively participate in real-world projects, including campaigns for community improvement, and are tasked with generating content for diverse platforms. Through hands-on collaboration with live clients and the utilization of tools such as Google AdWords and Facebook Advertising, students acquire practical experience in the realms of digital marketing and analytics. == vBook == A vBook is an eBook that is digital first media with embedded video, images, graphs, tables, text, and other media.
Web science
Web science is an emerging interdisciplinary field concerned with the study of large-scale socio-technical systems, particularly the World Wide Web. It considers the relationship between people and technology, the ways that society and technology co-constitute one another and the impact of this co-constitution on broader society. Web Science combines research from disciplines as diverse as sociology, computer science, economics, and mathematics. The Web Science Institute, founded at the University of Southampton by director Wendy Hall and colleagues, describes Web Science as focusing "the analytical power of researchers from disciplines as diverse as mathematics, sociology, economics, psychology, law and computer science to understand and explain the Web. It is necessarily interdisciplinary – as much about social and organizational behaviour as about the underpinning technology." A central pillar of Web science development is Artificial Intelligence or "AI". The current artificial intelligence that in development at the moment is Human-Centered, with goals to further professional development courses as well as influencing public policy. Artificial intelligence developers are focused on the most impactful uses of this technology, while also hoping to expedite the growth and development of the human race. An early definition was given by American computer scientist Ben Shneiderman: "Web Science" is processing the information available on the web in similar terms to those applied to natural environment. == Areas of activity == === Emergent properties === Philip Tetlow, an IBM-based scientist influential in the emergence of web science as an independent discipline, argued for the concept of web life, which considers the Web not as a connected network of computers, as in common interpretations of the Internet, but rather as a sociotechnical machine capable of fusing together individuals and organisations into larger coordinated groups. It argues that unlike the technologies that have come before it, the Web is different in that its phenomenal growth and complexity are starting to outstrip our capability to control it directly, making it impossible for us to grasp its completeness in one go. Tetlow made use of Fritjof Capra's concept of the 'web of life' as a metaphor. == Research groups == There are numerous academic research groups engaged in Web Science research, many of which are members of WSTNet, the Web Science Trust Network of research labs. Health Web Science emerged as a sub-discipline of Web Science that studies the role of the Web's impact on human's health outcomes and how to further utilize the Web to improve health outcomes. These groups focus on the developmental possibilities, provided through Web Science, in areas such as health care and social welfare. Discussion of web science has been widely adopted as a method in which the internet can have a real world impact in the field of medicine, currently coined Medicine 2.0. The World Wide Web acts as a medium for the spread and circulation of knowledge, though these various research groups consider themselves responsible for maintaining verifiable and testable knowledge. Using their knowledge of the healthcare system as well as web science, researchers are focused on formatting and structuring their knowledge in a way that is easily accessible throughout the internet. The World Wide Web is quickly evolving meaning that the information we provide and its formatting must also. Recognizing the overlap between both aspects, the spread of knowledge and development of the internet, allows us to properly display our knowledge in a manner that evolves as quickly as the internet and everyday medical research. The accessibility of the internet and quick development of knowledge must be companied with efficient formatting to allocate successful dissemination of information, as described by these various researcher groups. == Related major conferences == Association for Computing Machinery (ACM), Hypertext Conference (HT) sponsored by SIGWEB ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) International AAAI Conference on Weblogs and Social Media (ICWSM) The Web Conference (WWW) Association for Computing Machinery (ACM) Web Science Conference (WebSci)
CLEVER score
The CLEVER (Cross Lipschitz Extreme Value for nEtwork Robustness) score is a way of measuring the robustness of an artificial neural network towards adversarial attacks. It was developed by a team at the MIT-IBM Watson AI Lab in IBM Research and first presented at the 2018 International Conference on Learning Representations. It was mentioned and reviewed by Ian Goodfellow as well. It was adopted into an educational game Fool The Bank by Narendra Nath Joshi, Abhishek Bhandwaldar and Casey Dugan
Zero-overhead looping
In computer architecture, zero-overhead looping is a hardware feature found in some processors that enables loops to execute without the performance cost of traditional loop control instructions. Instead of software managing loop iterations, the processor's hardware handles repetition automatically, saving clock cycles and improving efficiency. This technique is commonly employed in digital signal processors (DSPs) and certain complex instruction set computer (CISC) architectures. == Background == In many instruction sets, a loop must be implemented by using instructions to increment or decrement a counter, check whether the end of the loop has been reached, and if not jump to the beginning of the loop so it can be repeated. Although this typically only represents around 3–16 bytes of space for each loop, even that small amount could be significant depending on the size of the CPU caches. More significant is that those instructions each take time to execute, time which is not spent doing useful work. The overhead of such a loop is apparent compared to a completely unrolled loop, in which the body of the loop is duplicated exactly as many times as it will execute. In that case, no space or execution time is wasted on instructions to repeat the body of the loop. However, the duplication caused by loop unrolling can significantly increase code size, and the larger size can even impact execution time due to cache misses. (For this reason, it's common to only partially unroll loops, such as transforming it into a loop which performs the work of four iterations in one step before repeating. This balances the advantages of unrolling with the overhead of repeating the loop.) Moreover, completely unrolling a loop is only possible for a limited number of loops: those whose number of iterations is known at compile time. For example, the following C code could be compiled and optimized into the following x86 assembly code: == Implementation == Processors with zero-overhead looping have machine instructions and registers to automatically repeat one or more instructions. Depending on the instructions available, these may only be suitable for count-controlled loops ("for loops") in which the number of iterations can be calculated in advance, or only for condition-controlled loops ("while loops") such as operations on null-terminated strings. === Examples === ==== PIC ==== In the PIC instruction set, the REPEAT and DO instructions implement zero-overhead loops. REPEAT only repeats a single instruction, while DO repeats a specified number of following instructions. ==== Blackfin ==== Blackfin offers two zero-overhead loops. The loops can be nested; if both hardware loops are configured with the same "loop end" address, loop 1 will behave as the inner loop and repeat, and loop 0 will behave as the outer loop and repeat only if loop 1 would not repeat. Loops are controlled using the LTx and LBx registers (x either 0 to 1) to set the top and bottom of the loop — that is, the first and last instructions to be executed, which can be the same for a loop with only one instruction — and LCx for the loop count. The loop repeats if LCx is nonzero at the end of the loop, in which case LCx is decremented. The loop registers can be set manually, but this would typically consume 6 bytes to load the registers, and 8–16 bytes to set up the values to be loaded. More common is to use the loop setup instruction (represented in assembly as either LOOP with pseudo-instruction LOOP_BEGIN and LOOP_END, or in a single line as LSETUP), which optionally initializes LCx and sets LTx and LBx to the desired values. This only requires 4–6 bytes, but can only set LTx and LBx within a limited range relative to where the loop setup instruction is located. ==== x86 ==== The x86 assembly language REP prefixes implement zero-overhead loops for a few instructions (namely MOVS/STOS/CMPS/LODS/SCAS). Depending on the prefix and the instruction, the instruction will be repeated a number of times with (E)CX holding the repeat count, or until a match (or non-match) is found with AL/AX/EAX or with DS:[(E)SI]. This can be used to implement some types of searches and operations on null-terminated strings.