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It's All about the Data
Published in James Luke, David Porter, Padmanabhan Santhanam, Beyond Algorithms, 2022
James Luke, David Porter, Padmanabhan Santhanam
With so much data available, it’s all too easy for the Algorithm Addict to fall off the wagon! Surely, all you need to do is gather as much data as possible and the algorithm will do the rest? No! It’s time to get back into rehab and remember that there is no magic algorithm and just throwing algorithms at data never delivers value. To successfully deliver AI solutions, you are going to need to learn a little more about data and how to deal with it. It really is all about the data, and in AI project, you can expect to invest up to 80% of the available resources just on accessing, understanding, cleansing, preparing and managing the data. Remember, once your model is trained, you are going to have to include most or all of the data transformations you put your training data through into your live data application before you can use it. Spoiler alert: if your data transformation code needed to look up massive tables of reference data to clean/augment the transaction data, so will your live model. If you need the application that model sits in to have sub second responses, you may want to rethink your business case.
Data Lakes: A Panacea for Big Data Problems, Cyber Safety Issues, and Enterprise Security
Published in Mohiuddin Ahmed, Nour Moustafa, Abu Barkat, Paul Haskell-Dowland, Next-Generation Enterprise Security and Governance, 2022
A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Abu Barkat Ullah
The enterprise data indicates the data shared by employees and partners in a company, master data, transaction data, and analytic data. Enterprise data quality has some characteristics, such as accuracy, consistency, completeness, timeliness, metadata management, and data lineage. Residence of enterprise data includes intranet, cloud, social media, data stores, traditional data warehouse, and file stores. Enterprise data lakes provide a centralized data repository, meaningful business insights, and superior business operations with the help of artificial intelligence (A.I.). Enterprise data lakes are used for data governance, A.I. application for business intelligence, predictive analysis, information hygiene (i.e., traceability and consistency), historical analysis, and future growth analysis.
Design and implementation of mobile money system using Near Field Communication (NFC)
Published in Amir Hussain, Mirjana Ivanovic, Electronics, Communications and Networks IV, 2015
In transactions between smartphone and EDC, merchant request can be omitted because programming in EDC is done from LLCP layer. Programming in LLCP layer gives more control of the data received or sent by an NFC reader in EDC. The data received from smartphone can be checked first. If there's an error in the data checking process, NFC reader will send "command not supported" APDU to smartphone. If there's no error in the data checking process, NFC reader will send "command normally completed" APDU to smartphone. In this transaction process, NFC reader emulates NFC tag. Tag emulation is used because P2P cannot be implemented perfectly by the CN-370S-2 NFC reader. Both transaction process between smartphone and smartphone, and transaction process between smartphone and NFC reader have similar sequence, transaction data transfer. In the transaction process between smartphone and smartphone, the transaction data transfer is done after merchant request completed. In the transaction process between smartphone and NFC reader, transaction data transfer is done after payer inputted amount and three digit random numbers. Transaction data transfer contains encrypted transaction data packet as shown in Figure 4.
S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis
Published in Connection Science, 2022
Shengting Wu, Yuling Liu, Ziran Zou, Tien-Hsiung Weng
Before calculating the technical indicators, we need to preprocess the traditional data sources, including clearing and filtering out some meaningless data to improve the quality of data. We choose five stocks of listed companies from EastMoney.com and crawl the historical trading data of five stocks from it. These transaction data include trading day, stock code, stock name, opening price, closing price, highest price, lowest price, adjusted closing price and trading volume. It is critical to remove unnecessary information and leave trading date, opening price, highest price, lowest price, closing price and trading volume.
Blockchain Adoption from an Interorganizational Systems Perspective – A Mixed-Methods Approach
Published in Information Systems Management, 2021
Flynn Werner, Marcus Basalla, Johannes Schneider, Demelza Hays, Jan Vom Brocke
Blockchain technology allows storing transaction data in a decentralized way, that disincentives manipulation of the data. Blockchain technology itself is a specific type of public ledger, comprised of unchangeable, digitally recorded data blocks which are chained together. In contrast to centralized transaction systems, the transaction information is not stored in one location but in a network of independent computers called nodes. Each node records, shares, and synchronizes transactions in its respective electronic ledger.