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This repository provides a guidance on integrating an Android app with a myPOS Smart device. Once integrated, the app will be able to communicate with the core device components in order to accept card payments (including but not limited to VISA, Mastercard, UnionPay International, JCB, Bancontact). myPOS-Smart-SDK complеments a Smart POS terminal in order to initiate transaction, complete all steps for processing payment, make refund to the customer card account and print a custom bill slip using the device printer. The built-in functionalities of myPOS-Smart-SDK allow you to send Payment Requests, manage operations for the first and second SAM component the myPOS Smart device has and print bitmap image integrated to the receipt (e.g. QR Code).

This repository provides a guidance on integrating an Android app with a myPOS Glass payment solution. Once integrated, the app will be able to communicate and call the main myPOS Glass functionalities in order to accept card payments (Limited to VISA and Mastercard). myPOS-Glass-SDK is a bridge between the applications and myPOS Glass app, converting the smart device into a POS machine, allowing to initiate transaction, complete all steps for processing payment, make refund to the customer card account and send a custom bill slip via mail or SMS. The built-in functionalities of myPOS-Glass-SDK allows you to accept payments, make refunds and void the last approved transaction.

Smart Commit JDBC Driver enables 'smartcommit' mode for all JDBC drivers. In Smart Commit mode a JDBC connection will execute all queries in autocommit mode until it encounters a write operations. From the first write operation until the following commit or rollback, the connection will operate in transactional mode. It will then switch back to autocommit and remain in autocommit until it once again encounters a write operation. Using a JDBC connection in Smart Commit mode will reduce the number of read locks needed. Operating a JDBC connection in Smart Commit mode effectively reduces the isolation level of a transaction to READ_COMMITTED for all queries that are executed *BEFORE* a write operation in a transaction. All queries that are executed *AFTER* a write operation in a transaction, will be executed using the configured isolation level of the JDBC connection.

Suji is a converter library from Japanese number notation to numerical value, and from numerical notation to Japanese Kansuji notation. Converter.values(src) to convert from Japanese number notation to numerical value: Japanese number notation can include Kansuji. The String "１つの価格が二兆30万五千十7円になります。" will be converted to two BigDecimal, 1 and 2000000005017. And also, 打率は三割二部五厘です。 will be a 0.325. The return value is a list of Suji Numeral objects. If the input string has no number notation, Suji returns a empty list. The Numeral object has three methods: value(), begin(), and end(): value(): a BigDecimal instance of a numerical value for the number notation. begin(): the begin index (int) of the found number notation at the input string. end(): the end index (int) of the found number notation. Converter.kansujis(src) to convert from numeric notation to Japanese Kansuji notation: The String 20兆30万五千十7円になります。 will be converted to the Kansuji string, 二十兆三十万五千十七. The boolean flag one is interpreted as whether to display the first character 一 or not. The output of Converter.kansujis('1000万', true) will be converted to 一千万, and the output of Converter.kansujis('1000万', false) will be converted to 千万. Note that kansujis does not support numerical notation after the decimal point. If the input string is 32.01, the output will 三十二, not 三十二割一厘. The return value is a list of Kansuji objects. If the input string has no number notation, Suji returns a empty list. The Kansuji object has three methods: value(), begin(), and end(): value(): a String instance of a Kansuji notation. begin(): the begin index (int) of the found number notation at the input string. end(): the end index (int) of the found number notation. Suji is a one-pass parser. That is, Suji parse a source text from the head to the end only once.

This repository provides a guidance on integrating an Android app with a myPOS Smart device. Once integrated, the app will be able to communicate with the core device components in order to accept card payments (including but not limited to VISA, Mastercard, UnionPay International, JCB, Bancontact). myPOS-Smart-SDK complеments a Smart POS terminal in order to initiate transaction, complete all steps for processing payment, make refund to the customer card account and print a custom bill slip using the device printer. The built-in functionalities of myPOS-Smart-SDK allow you to send Payment Requests, manage operations for the first and second SAM component the myPOS Smart device has and print bitmap image integrated to the receipt (e.g. QR Code).

This repository provides a guidance on integrating an Android app with a myPOS Smart device. Once integrated, the app will be able to communicate with the core device components in order to accept card payments (including but not limited to VISA, Mastercard, UnionPay International, JCB, Bancontact). myPOS-Smart-SDK complеments a Smart POS terminal in order to initiate transaction, complete all steps for processing payment, make refund to the customer card account and print a custom bill slip using the device printer. The built-in functionalities of myPOS-Smart-SDK allow you to send Payment Requests, manage operations for the first and second SAM component the myPOS Smart device has and print bitmap image integrated to the receipt (e.g. QR Code).

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

Welcome to the Blazegraph/TinkerPop3 project. The TP3 implementation has some significant differences from the TP2 version. The data model has been changed to use RDF*, an RDF reification framework described here: https://wiki.blazegraph.com/wiki/index.php/Reification_Done_Right. The concept behind blazegraph-gremlin is that property graph (PG) data can be loaded and accessed via the TinkerPop3 API, but underneath the hood the data will be stored as RDF using the PG data model described in this document. Once PG data has been loaded you can interact with it just like you would interact with ordinary RDF - you can run SPARQL queries or interact with the data via the SAIL API. It just works. The PG data model is also customizable via a round-tripping interface called the BlazeValueFactory, also described in detail in this document.

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.