#DataProcessingSystems

[sifytech]

Why you should integrate disparate business systems: 5 key reasons

As businesses continue diversifying and expanding, integrated systems will become increasingly vital in ensuring productivity, efficiency, and success. Read More. https://www.sify.com/digital-transformation/why-you-should-integrate-disparate-business-systems-5-key-reasons/

[placement-india]

3 Important Steps to Become a 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫:

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[hydralisk98]

Utalics' LibreVast "DataProcessingSystem"

"Fantasy" computer system inspired by the SEGA DreamCast, StarDragonModels' Cosmos, the Sanyo 3DO TRY, the OUYA, the Famicube and the Nintendo 64DD.

Specifications

48-bit RISC-V-like Juniper6 SDPm (symbolic data processor module) x2-x6

Using 12-bit words as most fundamental computer unit (the smallest four binary digits )

Twelve generic 12-bit registers ( A,B,C,D,E,F,U,V,W,X,Y,Z; )

Four special-use registers (48-bit program counter, 24-bit storage accumulator, links 4-bit register & 20-bit scientific notation coefficient )

Includes a deque data structure component that can store up to ~64 12-bit elements

A RISC-like ISA { Load value to register, Load from register to register, Load from memory to register, Store register value in memory, Compare register to register, Compare register with value, Branch if equal, Branch if less, Branch if more, Branch unconditionally, Add value to register, Add register to register, Subtract value from register, Substract register from register, Bitwise Shift right, Bitwise Shift left, Bitwise Rotate left, Bitwise Rotate right, deque INJECT, deque PUSH, deque POP, deque EJECT, deque PEEK, deque DROP, deque DUPLICATE, deque SWAP, deque OVER, deque ROTATE CLOCKWISE, deque ROTATE COUNTERCLOCKWISE, deque ROLL, deque BACKPEEK, deque REVERSE ROLL, deque REVERSE DUPLICATE, deque BACKSWAP, deque UNDER, deque BACK ROTATE CLOCKWISE, deque BACK ROTATE COUNTERCLOCKWISE, deque REVERSE DROP, deque PAD, deque REVERSE PAD;), NOT, NOR, NAND, AND, OR, XOR, Carry, ?, ?, ?, ?, ?, ?, Halt, Noop; }

64-bit wide instructions { 8-bit opcode, (6-8 extension?) 4-bit register, 48-bit data }

Expanded UTF-8 encoding

480x288p RGBA 12-bit/channel screen resolution at 60 FPS

144MW Unified Memory

48MW Video RAM for 48-bit programmable opacity display

48MW Audio RAM for 8D audio

4MW SRAM for libre bootloader & machine-level utilities

48GW Storage (using the last 16GW as swap)

DirectMemoryAccess feature

32-bit stereo sound

SAM= Symbolic Analog Monitor, secular overseer system daemon that handles much software time-sharing functions in a transparent and empowering manner

MAM= Magickal Agent Mentor, group of utilities for spiritual esoteric and user guidance

WAX= Wirebox Analog eXecutive, low-power analog processor for timeless processing

ZeraDPS (ZealOS-like operating system)

VeneraDIS (Linux-like desktop environment / window manager)

Sasha (Fish-like programmable shell)

Nucleus496 (Microkernel with Linux-like reliability)

Brainstorm for Angora

Programmable Autonomous Organizations (eq. to DAOs)

Mesa (multimedia and hypermedia utilities)

Macroware Veina (rich media editor with cell editing & multi-user wiki editing support, between LibreOffice and NVIM)

4Kard (cardfile / hypercard bulletin board and session time-sharing server system)

Fidel (high-level programming language quite similar to the likes of F#)

Matra (OpenXanadu equivalent as global information system / public-access wiki)

Prospero (multi-player game series by Vixen softworks aka Valve)

Solarmonk (single-player game series by Magnata softworks aka Bethesda)

Milix 3D modelling libre software similar to Blender and AutoCAD

INMOS (Assyrian/Morocco own competitor to ITS & CLADO, from '68)

CLADO (first popular operating system in Angora, developed in '59)

ITS (competitor to CLADO, developed in '63)

Perseus (successor / half-merger between most operating systems, timestamped in 1970)

COS-5 (COS-310 DIBOL environment wth Tmux windowing, TAKO Emacs text editor & Bish shell)

SASS (early Windows equivalent from EBM and Macroware, not very popular)

Van (Win98/ME/ArcaOS-like, still not very popular)

Synod (Ring-0 Microsoft Bob equivalence with very cute graphics and successful with the youth)

Nomad (Uxn / Plan9 / Inferno)

Tiger (C-like programming language, similar to Nim, Lobster, Python and Lua)

Chateau (OpenIndiana / Haiku / PhantomOS / macOS)

Arbav subsystem { affirmation-based emulation, voice-operated system and analog GAI modular section }

Symbolic Algebra system { Fractions, soviet balanced ternary operations, simplifier, garbage collection, arbitrary precision arithmetic, mathematical algorithms & special functions, polynomials, artificial neural network emulation alternative, mathematical constants, optimizations, linear & non-linear equations, integral transformations, series operations, matrix operations, statistical computation, plotting graphs, charts/diagrams?, differential equations, signal processing, sound synthesis, SIN/COS/TAN, constraint logic programming, API library of addon functions; }

FastMath Co-Operative Processing Unit { Multiply, Division, Floating Point Arithmetics, Random Number Generator, POSIX-compliance, optimized code generation, string manipulations, base converters, bitwise logic operators?, square roots, exponents, logarithmic, trigonometrics; }

BASIC + DIBOL

PacoLisp (tiny & versatile low-level) & MiraLisp (much infrastructure & documentation)

HaxelN (virtual memory editor)

Hixi (powerful scripting spreadsheet editor, not by Macroware)

Nao (open media document specification like DolDOC)

Maskoch, disk / partition / physical media editor

Zira I/O, bus, drivers and card expansions

PETSCII-like graphical primitives set

Athena (JVM eq.)

Argdown (extended LaTex / Markdown specification)

Witness (Swift-like)

Mao (visual programming language between Fortran, Turtle graphics and AGAT Robic)

Ruin (very advanced debugger & cryptoanalysis utility suite)

Monada (a famous code poem written in the seventies, similar to a benchmarking "Hello, World!" program for synthetics)

SMall-Talk (professional programming language for databases and parallelist mainframe operations)

Adwa (Multilingual symbolic programming system layer similar to IPL)

'Maniac' operating system (MUSIC/SP eq.)

[loginworksoftware-blog]

Data Analytics or Data Science – Which is More Affordable for Startups?

Startups tend to collect large amounts of data to pave the path of their progress at a faster speed but have limited resources to store data. All they desire is predictive analysis because they want to track the behavior of potential customers for maybe a year or two.

WHAT ROLES DO DATA SCIENTISTS AND DATA ANALYSTS PLAY?

To answer the question of which is a more affordable option for startups, a Data Scientist or a Data Analyst, let us look at the job profile and scope of both these professionals:

The Data Science process involves

:Step #1: Answering queries

The Scientist’s machine learning model has to answer a question or solve a problem. Of course, there will be many permutations and combinations of data sets to deal with different queries. So the data model must be a comprehensive set of parameters to deal with all eventualities.

Step #2: Collecting data

Web scrapping or collection of real-time Big Data is the next step that a Data Scientist will undertake. Sample streamed data will be collected initially to test and retest the data model.

Step #3: Reviewing the Data

Even the best data models can collect irrelevant data. At times web users enter wrong information either because of typo errors or intentional falsification. This data is collected along with the rest of the information. Reviewing the data for relevancy and accuracy is the next step that a Data Scientist has to perform.

Step #4: Cleaning the data

This stage involves:

Co-relating different data sets from multiple sources for logical processes.

Checking for redundancies or unusual patterns so that, as a Scientist, you can add parameters to deal with these situations.

Evaluating the relevance of the data to the client’s needs.

Deciding whether the data collected is of any use or fresh data has to be collected for testing your machine learning model.

Step #5. Testing the Data

Storing the information so that it can be used for retesting and reporting is the next stage in the Data Science process. The common tools used by Data Scientists are R, SQL, and Python. The stored data is used in subsets for pre-processing. So you have to formulate scripts that will automatically correct the anomalies and reformat the data into logical, quantifiable data sets. This involves:

Building the data model to answer specific queries.

Cross-validating the data.

Using regression analysis to test the data.

Comparing the efficacy of your algorithm against other logical techniques.

Finalizing your model once it shows a high level of efficiency in producing the desired results.

The Data Scientist has to also consider issues like logistics, the privacy of data, and accessibility protocols while finalizing the data model. Once your data model is honed to perfection and all the parameters are in place, it is time to test it against real-time live data.

Step #6: Risk assessment

Every production unit or service industry has several key players whose hand is involved in the finished product. Suppliers of raw material, labor, warehouses, distribution systems, marketing and sales, courier services, wholesalers, retailers, and many other factors are involved in the supply chain. Assessing the risk and checking the credibility of all the external players is also a very important role that a Data Scientist plays. In fact, this is one of the most crucial roles of a Scientist. Without risk assessment, your client will not know if any of the partners have compliance issues.

The role of the Data Analyst

If Data Science is the toolbox then Data Analysis is the set of tools inside the box. The typical tasks that a Data Analyst performs are:

More focused data analysis to answer specific queries and needs of a particular.

Unlike a Data Scientist who will repopulate the databank for retesting the model, the Data Analyst will sort through the existing information to search for the data sets that would fit the desired parameters. Which means that the model is designed with a very specific query in mind and the data collected has to be relevant to that query. So the scope of mining and testing is limited compared to Data Science.

The Data Analysis process involves sorting through existing data like past experiences, current trends, desired markets that the client wants to tap, etc. The aim is solely to track customer behavior, their preferences, seasonal ups and downs in demand, etc. in order to implement short-term marketing strategies. The tools usually used by Data Analysts are R, Excel, Python, and Tableau.

So Data Science involves a number of specialists who work as a team. They use a mix-and-match of data models and techniques to get the desired information, including the tracking of customer online payment activities. Data Science uses statistical formulae to access, process, and manipulate data so that the Analyst can query it for client-specific analysis and reporting.

Based on the skillset, a Data Scientist can be a Data Researcher, Data Developers, Creative Developer, Data Businessperson, or Data Scientist. A Data Analyst can take on roles like Database Administrator, Data Architect, Operations, or Analytics Engineer.

So when you look at the Data Scientist’s scope of work you can guess that it is a more specialized field and requires a deeper knowledge of Business Intelligence techniques and programming. Data Scientists in most cases work in an agency that offers specialist services to business organizations.

Whereas, there are many companies today who employ an in-house Data Analyst to help them to globalize their market and create brand value. Since the Analysts role is limited, the remuneration expected is also lower than what a company might have to pay a Scientist. Also, there are many freelance Data Analysts who offer their expertise for affordable fees on a project basis. Startups, with their limited resources, will usually prefer to employ the services of a Data Analyst because it is a more affordable choice and because they have short-term goals that need to be met quickly.

[loginwork-blog]

Loginworks softwares will be provided how can be use of data processing and terms of Big Data refers to the large amounts of data in which traditional data processing procedures and tools would not be able to handle.At the present time, Data is the need of the world. You know, each day we are creating 2.5 quintillion bytes of data in the present era, and that’s huge.It is the process of transforming raw data into information by performing some actual data manipulation techniques.

[business-to-business-discussion]

Pin points to consider before hiring an outsourcing company for data entry tasks.

[loginwork-blog]

Big Data is an immensely popular talking point but what are we are really discussing From a security perspective on big data. A growing number of companies are using this technology to store and analyse petabytes of data including. Loginwork Software data and social media content to gain better insight about their customers and their business.