Big Data Science

25 Sep 2023 17:59

😎💥Cool AI services for working with Big Data
AskEdith - works in a “Data Chat” mode where you can connect to your data sources and ask questions in natural language. AskEdith provides answers in a variety of formats, including visualizations. Capable of analyzing large volumes of data and connecting to various databases and CRM systems such as Google Sheets, Airtable, PostgreSQL, MySQL, SQL Server, Snowflake, BigQuery, Redshift and others.
Tomat.AI - allows you to analyze large CSV files without the need for programming or creating a special formula. You can easily filter, sort, merge multiple files and create visualizations in just a few clicks.
Coginiti - Creates SQL queries, explains their meaning and, if necessary, optimizes performance. Supports access to various data warehouses, Redshift, Microsoft, Snowflake, IBM, BigQuery, Yellowbrick, Databricks and others. In addition, there is a repository for storing SQL queries.
Formula God - Artificial intelligence that integrates into Google Sheets, including working with data without the need to use formulas. You can create queries using normal spoken language.
Simple ML for Sheets – With these extensions, anyone can use machine learning in Google Sheets rather than define programming and machine learning. But it is also suitable for experts if they want to quickly select a task for small amounts of data. Developed by the TensorFlow Solution Forest team.

Big Data Science

17 Sep 2023 17:59

🔎📝Data standardization: advantages and disadvantages
Data standardization is the process of transforming data so that it has a standard distribution with a mean of zero and a standard deviation of 1. This process is an important part of data preprocessing and is often used in data mining and machine learning.
Benefits of data standardization:
1. Improve the convergence of algorithms: Many machine learning algorithms and statistical methods work better on data that has a standard distribution. Standardization can help improve the convergence of algorithms and speed up the training process of models.
2. Improved interpretability: When data is standardized, coefficients in regression models or weights in neural networks become more interpretable. This makes it easier to analyze the impact of each variable on the result.
3. Eliminating large scale differences: Standardization helps to avoid problems associated with large scale differences between variables. If one variable has values thousands of times greater than another, it can skew the results of the analysis.
4. Working with some algorithms: Some algorithms, such as support vector machine (SVM) and gradient descent, are sensitive to the scale of the data. Standardization can help make these algorithms more stable and efficient.

Disadvantages of data standardization:
1. Loss of information: When standardizing data, we lose information about the actual units of measurement of the variables. In some cases this may be important information for interpreting the results.
2. Impact on outliers: Standardization can increase the impact of outliers if they are present in the data. This may affect the stability and accuracy of the models.
3. Dependence on the choice of method: There are several standardization methods, such as Z-transform, min-max scaling, etc. Choosing the wrong method can greatly affect the analysis results.

Overall, data standardization is an important tool in data analytics and machine learning that can improve the performance of algorithms and make results easier to interpret. However, its application should be considered taking into account the specific task and data characteristics.

Big Data Science

10 Sep 2023 18:02

⚡️🔥😎Tools for image annotation in 2023
V7 Labs is a tool for creating accurate, high-quality datasets for machine learning and computer vision projects. Its wide range of annotation features allows it to be used in many different areas.
Labelbox is the most powerful vector labeling tool targeting simplicity, speed, and a variety of use cases. It can be set up in minutes, scale to any team size, and iterate quickly to create accurate training data.
Scale - With this annotation tool, users can add scales or rulers to help determine the size of objects in an image. This is especially useful when studying photographs of complex structures, such as microscopic organisms or geological formations.
SuperAnnotate is a powerful annotation application that allows users to quickly and accurately annotate photos and videos. It is intended for computer vision development teams, AI researchers, and data scientists who annotate computer vision models. In addition, SuperAnnotate has quality control tools such as automatic screening and consensus checking to ensure high-quality annotations.
Scalabel - Helps users improve accuracy with automated annotations. It focuses on scalability, adaptability and ease of use. Scalabel's support for collaboration and version control allows multiple users to work on the same project simultaneously.

Big Data Science

03 Sep 2023 17:59

🔎🤔📝A little about unstructured data: advantages and disadvantages
Unstructured data is information that does not have a clear organization or format, which distinguishes it from structured data such as databases and tables. Such data can be presented in a variety of formats such as text documents, images, videos, audio recordings, and more. Examples of unstructured data are emails, social media messages, photographs, transcripts of conversations, and more.
Benefits of unstructured data:
1. More information: Unstructured data can contain valuable information that cannot be presented in a structured form. This may include nuance, context, and emotional aspects that may be missing from structured data.
2. Realistic representation: Unstructured data can reflect the real world and the natural behavior of people. They allow you to capture complex interactions and manifestations that can be lost in simplified structured data.
3. Innovation and research: Unstructured data provides a huge opportunity for innovation and research. The analysis of such data can lead to the discovery of new patterns, connections and insights.

Disadvantages of unstructured data:
1. Complexity of processing: Due to the lack of a clear structure, the processing of unstructured data can be complex and require the use of specialized methods and tools.
2. Difficulties in analysis: Extracting meaning from unstructured data can be more difficult than from structured data. It is required to develop algorithms and models for efficient interpretation of information.
3. Privacy and Security Issues: Unstructured data may contain sensitive information and may be more difficult to manage in terms of security and privacy.

Thus, the need to work with unstructured data depends on the specific tasks and goals of the organization. In some cases, the analysis and use of unstructured data can lead to valuable insights and benefits, while in other situations it may be less useful or even redundant.

Big Data Science

25 Aug 2023 17:59

📚⚡️Vertical scaling: advantages and disadvantages
Data vertical scaling is a database scaling approach in which performance gains are achieved by adding resources (eg, processors, memory) to existing system nodes. This approach focuses on improving system performance by increasing its resources within each node rather than adding new nodes.
Benefits of vertical data scaling:
1. Improved performance: Adding resources to existing nodes allows you to process more data and requests on a single server. This can result in improved responsiveness and overall system performance.
2. Ease of Management: Compared to scaling out (adding new servers), scaling up is less difficult to manage because it doesn't require the same degree of configuration and synchronization across different nodes.
3. Infrastructure Cost Savings: Adding resources to existing servers can be more cost effective than buying and maintaining additional servers.

Disadvantages of vertical data scaling:
1. Resource limit: Vertical scaling has limits determined by the maximum performance and resources of an individual node. Sooner or later, you can reach a point where further increase in resources will not lead to a significant improvement in performance.
2. Single-point-of-failure: If the node to which resources are being added goes down, this can lead to serious problems with the availability of the entire system. In horizontal scaling, the loss of one node does not have such a significant impact.
3. Limited scalability: As the load grows, more and more resources may need to be added, which can eventually become inefficient or costly.
4. Resource limit: With vertical scaling, the resource that is the most bottleneck can only be increased up to a certain limit. If this is the resource that limits performance, then additional resources may be spent inefficiently.

Big Data Science

20 Aug 2023 17:59

🔎📝 DBT framework: advantages and disadvantages
DBT (Data Build Tool) is an open data analysis framework that facilitates the process of preparing and processing data before analytical queries. DBT was developed taking into account the features of modern analytical practices and is focused on working with data in the environment of data warehouses and analytical databases. The main goal of DBT is to provide practical tools for managing and transforming data in the preparation of an analytical environment.
DBT Benefits:
1. Modularity and manageability: DBT allows you to create data modules that can be easily reused and extended. This makes it easier to manage and maintain the analytical infrastructure.
2. Versioning and change management: DBT supports code and documentation versioning, which makes change management and collaboration more efficient.
3. Automation of the ETL process: DBT provides tools to automate the data extraction, transformation and loading (ETL) process. This saves time and effort on routine tasks.
4. Dependency Tracking: DBT automatically manages dependencies between different pieces of data, making it easy to update and maintain the consistency of the analytics environment.
5. Use of SQL: DBT uses SQL to describe data transformations, making it accessible to analysts and developers.

DBT Disadvantages:
1. Limitations of complex calculations: DBT is more suitable for data transformation and preparation than for complex calculations or machine learning.
2. Complexity for Large Projects: Large projects with large amounts of data and complex dependencies between tables may require additional configuration and management.
3. Complexity of implementation: DBT implementation can take time and resources to set up and train employees.

Overall Conclusion: DBT is a powerful tool for managing and preparing data in an analytics environment. It provides many benefits, but may be less suitable for complex calculations and large projects. Before using DBT, it is recommended to carefully study its functionality and adapt it to the specific needs of the organization.

Big Data Science

12 Aug 2023 18:01

⚔️⚡️Comparison of Spark Dataframe and Pandas Dataframe: advantages and disadvantages
Dataframes are structured data objects that allow you to analyze and manipulate large amounts of information. The two most popular dataframe tools are Spark Dataframe and Pandas Dataframe.
Pandas is a data analysis library in the Python programming language. Pandas Dataframe provides an easy and intuitive way to analyze and manipulate tabular data.
Benefits of Pandas Dataframe:
1. Ease of use: Pandas offers an intuitive and easy to use interface for data analysis. It allows you to quickly load, filter, transform and aggregate data.
2. Rich integration with the Python ecosystem: Pandas integrates well with other Python libraries such as NumPy, Matplotlib and Scikit-Learn, making it a handy tool for data analysis and model building.
3. Time series support: Pandas provides excellent tools for working with time series, including functions for resampling, time windows, data alignment and aggregation.
Disadvantages of Pandas Dataframe:
1. Limited scalability: Pandas runs on a single thread and may experience performance limitations when working with large amounts of data.
2. Memory: Pandas requires the entire dataset to be loaded into memory, which can be a problem when working with very large tables.
3. Not suitable for distributed computing: Pandas is not designed for distributed computing on server clusters and does not provide automatic scaling.

Apache Spark is a distributed computing platform designed to efficiently process large amounts of data. Spark Dataframe is a data abstraction that provides a similar interface to Pandas Dataframe, but with some critical differences.
Benefits of Spark Dataframe:
1. Scalability: Spark Dataframe provides distributed computing, which allows you to efficiently process large amounts of data on server clusters.
2. In-instant computing: Spark Dataframe supports "in-memory" operations, which can significantly speed up queries and data manipulation.
3. Language Support: Spark Dataframe supports multiple programming languages including Scala, Java, Python, and R.
Disadvantages of Spark Dataframe:
1. Slightly slower performance for small amounts of data: Due to the overhead of distributed computing, Spark Dataframe may show slightly slower performance when processing small amounts of data compared to Pandas.
2. Memory overhead: Due to its distributed nature, Spark Dataframe requires more RAM compared to Pandas Dataframe, which may require more powerful data processing servers.

Big Data Science

06 Aug 2023 17:59

💥ConvertCSV is a universal tool for working with CSV
ConvertCSV is an excellent solution for processing and converting CSV and TSV files into various formats, including: JSON, PDF, SQL, XML, HTML, etc.
It is important to note that all data processing takes place locally on your computer, which guarantees the security of user data. The service also provides support for Excel, as well as command-line tools and desktop applications.

Big Data Science

31 Jul 2023 17:59

💥🌎TOP DS-events all over the world in August
Aug 3-4 - ICCDS 2023 - Amsterdam, Netherlands - https://waset.org/cheminformatics-and-data-science-conference-in-august-2023-in-amsterdam
Aug 4-6 - 4th International Conference on Natural Language Processing and Artificial Intelligence - Urumqi, China - http://www.nlpai.org/
Aug 7-9 - Ai4 2023 - Las Vegas, USA - https://ai4.io/usa/
Aug 8-9 - Technology in Government Summit 2023 - Канберра, Австралия - https://www.terrapinn.com/conference/technology-in-government/index.stm
Aug 8-9 - CDAO Chicago - Chicago, USA - https://da-metro-chicago.coriniumintelligence.com/
Aug 10-11 - ICSADS 2023 - New York, USA - https://waset.org/sports-analytics-and-data-science-conference-in-august-2023-in-new-york
Aug 17-19 - 7th International Conference on Cloud and Big Data Computing - Manchester, UK - http://www.iccbdc.org/
Aug 19-20 - 4th International Conference on Data Science and Cloud Computing - Chennai, India - https://cse2023.org/dscc/index
Aug 20-24 - INTERSPEECH - Dublin, Ireland - https://www.interspeech2023.org/
Aug 22-25 - International Conference On Methods and Models In Automation and Robotics 2023 - Мендзыздрое, Польша - http://mmar.edu.pl/

Big Data Science

22 Jul 2023 17:58

😎📊Visualization no longer requires coding
Flourish Studio is a tool for creating interactive data visualizations without coding
With this tool, you can create dynamic and visual graphs, graphs, maps, and other visual elements.
Flourish Studio provides an extensive selection of pre-made templates and animations, as well as an easy-to-assemble visual editor. It can easily turn on and enjoy entertainment.
Cost: #free (no paid plans).

Big Data Science

17 Jul 2023 17:58

📊⚡️Open source data generators
Benerator - test data generation software solution for testing and training machine learning models
DataFactory is a project that makes it easy to generate test data to populate a database as well as test AI models
MockNeat - provides a simple API that allows developers to programmatically create data in json, xml, csv and sql formats.
Spawner is a data generator for various databases and AI models. It includes many types of fields, including those manually configured by the user

Big Data Science

30 Jun 2023 17:59

💥📖TOP DS-events all over the world in July:
Jul 7-9 - 2023 IEEE the 6th International Conference on Big Data and Artificial Intelligence (BDAI) - Zheijiang, China - http://www.bdai.net/
Jul 11-13 - International Conference on Data Science, Technology and Applications (DATA) - Rome, Italy - https://data.scitevents.org/
Jul 12-16 - ICDM 2022: 23th Industrial Conf. on Data Mining - New York, NY, USA - https://www.data-mining-forum.de/icdm2023.php
Jul 14-16 - 6th International Conference on Sustainable Sciences and Technology - Istanbul, Turkey - https://icsusat.net/home
Jul 15-19 - MLDM 2023: 19th Int. Conf. on Machine Learning and Data Mining. New York, NY, USA - New York, USA - http://www.mldm.de/mldm2023.php
Jul 21-23 - 2023 7th International Conference on Artificial Intelligence and Virtual Reality (AIVR2023) - Kumamoto, Japan - http://aivr.org/
Jul 23-29 - ICML - International Conference on Machine Learning - Honolulu, Hawai'I - https://icml.cc/
Jul 27-29 - 7th International Conference on Deep Learning Technologies - Dalian, China - http://www.icdlt.org/
Jul 31-Aug 1 - Gartner Data & Analytics Summit - Sydney, Australia - https://www.gartner.com/en/conferences/apac/data-analytics-australia

Big Data Science

23 Jun 2023 17:59

📊📈📉🤖Pandas AI - AI library for Big Data analysis
Pandas AI is a Python library with built-in generative artificial intelligence or language model.
How it works: in the code editor, you can ask any question about data in natural language and, without writing code, you will get a ready-made answer based on your data.
You can install Pandas AI with the following command:
pip install pandasai
After installation, you need to import the pandasai library and the LLM (Large Language Model) function:
import pandas as pd
from pandasai import PandasAI
from pandasai.llm.openai import OpenAI
However, Pandas AI does not position itself as a replacement for Pandas. As the developers note, this is more of an improvement for the standard Pandas.
The developers also warn that the entire data frame is transmitted along with the question each time, so the solution is far from ideal for processing large data sets.

Big Data Science

16 Jun 2023 17:59

😎🧠⚡️One of the best startups for generating synthetic data for various industries
Hazy is a UK-based synthetic data generation startup that aims to train raw banking data for fintech industries.
Tonic.ai - offers an automated and anonymous way to synthesize data for use in testing and developing various software. This platform also implements database de-identification, which means filtering personal data (PII) from real data, as well as protecting customer privacy.
Mostly.AI is a Vienna-based synthetic data platform that serves industries such as insurance, banking, and telecommunications. It provides cutting-edge AI and top-notch privacy by extracting patterns and structure from source data to prepare vastly different datasets.
YData is a Portuguese startup that helps data scientists solve the problems of poor data quality or access to large user data with scalable AI solutions. When performing tests such as inference attacks, YData engineers are responsible for any risks of leaking or re-identifying an identity. Therefore, they use the TSTR (Train Synthetic Test Real) method, which evaluates the possibility of using AI-generated data to train predictive models.
Anyverse - creates a synthetic 3D environment. Anyverse renders and sets up various scenarios for sample data using a ray tracing engine. The technology calculates the interaction of light rays with scene objects at the physical level.
Rendered.AI is a startup that generates physics-based synthetic datasets for satellites, autonomous vehicles, robotics and healthcare.
Oneview is an Israeli data science platform that uses satellite imagery and remote sensing technologies for defense intelligence.

Big Data Science

09 Jun 2023 17:59

📖💡🤔 Top 6 Datasets for Computer Vision
CIFAR-10 - contains 60 thousand 32x32 color images with 10 classes (animals and real world objects). Each class consists of 6000 images. This dataset contains 50,000 training images and 10,000 test images. The classes are mutually exclusive and there is no overlap between them.
Kinetics-700 - This is a large array of videos, consisting of 650,000 clips describing 700 classes of human actions. The video includes human-thing interactions such as playing musical instruments and human-human interactions such as hugging. Each activity class contains at least 700 video clips, and each clip is annotated with an activity class that is longer than 10 seconds.
IMDB-Wiki - One of the largest publicly available datasets of human faces with gender, age and name. In total it contains 523051 images, 460723 faces are photos of 20284 celebrities from IMDb and 62328 celebrities from Wikipedia.
Cityscapes is a database containing a diverse set of stereographic video clips recorded on the streets of fifty cities. The clips were filmed for a long time under various lighting and weather conditions. Cityscapes contains semantic segmentation of object instances with pixel precision for 30 classes divided into 8 categories. It provides 5000 frame per pixel annotation and 20000 frame approximate annotation.
Fire and Smoke Dataset - This is a dataset of over seven thousand unique HD resolution images. It consists of photos of starting fires and smoke taken by mobile phones in real situations. The pictures were taken in a wide range of lighting and weather conditions. This data set can be used for fire and smoke recognition and detection, as well as anomaly recognition.
FloodNet Dataset - This array consists of high resolution images taken from unmanned drones. The images contain detailed semantic annotations of damage caused by hurricanes.

Big Data Science

20 Sep 2023 17:59

🤔Numexpr: advantages and disadvantages
NumExpr is a library for performing fast and efficient calculations using NumPy expressions in Python. It is designed to speed up calculations, especially when working with large amounts of data.
Advantages:
1. High Performance: NumExpr provides high performance by compiling and optimizing expressions, allowing you to perform operations on arrays of data much faster than using pure Python or even NumPy.
2. Multi-threading support: NumExpr supports multi-threading, which allows you to use multi-threaded calculations and improve performance when processing parallel tasks.
3. Minimal memory use: The library allows you to perform calculations with minimal use of RAM, which is especially important when working with large data.
4. NumPy Integration: NumExpr integrates easily with the NumPy library, making it easy to use in existing codebases.
5. Ease of Use: NumExpr's syntax is very similar to NumPy's, so for many users there is no need to learn a new language or API.

Flaws:
1. Limited functionality: NumExpr provides only a limited set of operators and functions, so not all operations can be optimized with it. For example, you cannot use arbitrary user-defined functions.
2. Difficult to Debug: Since the NumExpr code runs inside a dedicated virtual stack, debugging can be challenging if errors occur.
3. Not always the optimal choice: It is not always advisable to use NumExpr. In some cases, especially with small amounts of data or complex operations, using pure Python or NumPy may be more convenient and readable.

Big Data Science

14 Sep 2023 17:59

👱‍♂️⚡️The DeepFakeFace dataset has become publicly available
DeepFakeFace(DFF) is a dataset that serves as the basis for training and testing algorithms designed to detect deepfakes. This dataset is created using various advanced diffusion models.
The authors claim that they analyzed the DFF dataset and proposed two evaluation methods to evaluate the effectiveness and adaptability of deepfake recognition tools.
The first method tests whether an algorithm trained on one type of fake image can recognize images generated by other methods.
The second method evaluates the algorithm's performance on non-ideal images, such as blurry, low-quality, or compressed images.
Given the varying results of these methods, the authors highlight the need for more advanced deepfake detectors.


🧐 HF: https://huggingface.co/datasets/OpenRL/DeepFakeFace

🖥 Github: https://github.com/OpenRL-Lab/DeepFakeFace

📕 Paper: https://arxiv.org/abs/2309.02218

Big Data Science

07 Sep 2023 17:59

🔎📖📝A little about structured data: advantages and disadvantages
Structured data is information organized in a specific form, where each element has well-defined properties and values. This data is usually presented in tables, databases, or other formats that provide an organized and easy-to-read presentation.
Benefits of structured data:
1. Easy Organization and Processing: Structured data has a clear and organized structure, making it easy to organize and process. This allows you to quickly search, sort and analyze information.
2. Easy to store: Structured data is easy to store in databases, Excel spreadsheets, or other specialized data storage systems. This provides structured data with high availability and persistence.
3. High accuracy: Structured data is usually subject to quality control and validation, which helps to minimize errors and inaccuracies.

Disadvantages of structured data:
1. Restriction in information types: Structured data works well for storing and processing data with clear structures, but may be inefficient for storing information that does not lend itself to rigid structuring, such as text, images, or audio.
2. Dependency on predefined structure: Working with structured data requires a well-defined schema or data structure. This limits their applicability in cases where the data structure can change dynamically.
3. Difficulty of Integration: Combining data from different sources with different structures can be a complex task that requires a lot of time and effort.
4. Inefficient for some types of tasks: For some types of data analysis and processing, especially those related to unstructured information, structured data may be inefficient or even inapplicable.

Big Data Science

31 Aug 2023 17:59

🌎TOP DS-events all over the world in September
Sep 12-13 - Chief Data & Analytics Officers, Brazil – San Paulo, Brazil - https://cdao-brazil.coriniumintelligence.com/
Sep 12-14 - The EDGE AI Summit - Santa Clara, USA - https://edgeaisummit.com/events/edge-ai-summit
Sep 13-14 - DSS Hybrid Miami: AI & ML in the Enterprise. Miami, FL, USA & Virtual – Miami, USA - https://www.datascience.salon/miami/
Sep 13-14 - Deep Learning Summit - London, UK - https://london-dl.re-work.co/
Sep 15-17 - International Conference on Smart Cities and Smart Grid (CSCSG 2023) – Changsha, China - https://www.icscsg.org/
Sep 18-22 - RecSys – ACM Conference on Recommender Systems – Singapore, Singapore - https://recsys.acm.org/recsys23/
Sep 21-23 - 3rd World Tech Summit on Big Data, Data Science & Machine Learning – Austin, USA - https://datascience-machinelearning.averconferences.com/

Big Data Science

23 Aug 2023 17:59

💥😎Recently, a dataset appeared on the network for video segmentation using motion expressions
MeViS is a large-scale motion expression-driven video segmentation dataset that focuses on object segmentation in video content based on a sentence that describes the motion of objects. The dataset contains many motion expressions to indicate targets in complex environments.

Big Data Science

16 Aug 2023 17:59

🔉💥Opened access to more than 1.5 TB of labeled audio datasets
At Wonder Technologies, a significant amount of time has been spent by developers building deep learning systems that can perceive the world through audio signals. From applying sound-based deep learning to teaching computers to recognize emotions in sound, the company has used a wide range of data to create APIs that can function effectively even in extreme audio environments. According to the developers, the site provides a list of datasets that turned out to be very useful in the course of research and which were used to improve performance sound models in real scenarios.

Big Data Science

09 Aug 2023 17:59

📝🔎Data Observability: advantages and disadvantages
Data Observability is the concept and practice of providing transparency, control and understanding of data in information systems and analytical processes. It aims to ensure that data is accessible, accurate, up-to-date, and understandable to everyone who interacts with it, from analysts and engineers to business users.
Benefits of Data Observability:
1. Quickly identify and fix problems: Data Observability helps you quickly find and fix errors and problems in your data. This is especially important in cases where even small failures can lead to serious errors in analytical conclusions.
2. Improve the quality of analytics: Through data control, analysts can be confident in the accuracy and reliability of their work results. This contributes to making more informed decisions.
3. Improve Collaboration: Data Observability creates a common language and understanding of data across teams ranging from engineers to business users. This contributes to better cooperation and a more efficient exchange of information.
4. Risk Mitigation: By ensuring data reliability, Data Observability helps to mitigate the risks associated with bad decisions based on inaccurate or incorrect data.
Disadvantages of Data Observability:
1. Complexity of implementation: Implementing a Data Observability system can be complex and require time and effort. This may require changes in the data architecture and the addition of additional tools.
2. Costs: Implementing and maintaining a data observability system can be a costly process. This includes both the financial costs of tools and the costs of training and staff maintenance.
3. Difficulty of scaling: As the volume of data and system complexity grows, it can be difficult to scale the data observability system.
4. Difficulty in training staff: Staff will need to learn new tools and practices, which may require time and training.

In general, Data Observability plays an important role in ensuring the reliability and quality of data, but its implementation requires careful planning and balancing between benefits and costs.

Big Data Science

02 Aug 2023 17:59

📝🔎Problems and solutions of text data markup
Text data markup is an important task in machine learning and natural language processing. However, she may encounter various problems that can make the process difficult and complicated. Some of these problems and possible solutions are listed below:
1. Subjectivity and ambiguity: Text markup can be subjective and ambiguous, as different people may interpret the content differently. This can lead to inconsistencies between markups.
Solution: To reduce subjectivity, it is necessary to provide markers with clear marking instructions and rules. Discussing and revising the results between scalers can also help identify and resolve ambiguities.
2. High cost and time consuming: Labeling text data can be costly and time consuming, especially when working with large datasets.
Solution: Using automatic labeling and machine learning methods for the initial phase can significantly reduce the amount of human work. It is also worth paying attention to the possibility of using crowdsourcing platforms to attract more markers and speed up the process.
3. Lack of standards and formats: There is no single standard for markup of textual data, and different projects may use different markup formats.
Solution: Define standards and formats for marking up data in your project. Follow common standards such as XML, JSON, or IOB (Inside-Outside-Beginning) to ensure compatibility and easy interoperability with other tools and libraries.
4. Lack of training for markups: Marking up textual data may require expert knowledge or experience in a particular subject area, and it is not always possible to find markups with the necessary competence.
Solution: Provide markups with learning material and access to resources to help them better understand the context and specifics of the task. You can also consider training markups within the team to improve markup quality.
5. Heterogeneity and imbalance in data: In some cases, labeling can be heterogeneous or unbalanced, which can affect the quality of training models.
Solution: Make an effort to balance the data and eliminate heterogeneity. This may include collecting additional data for smaller classes or applying data augmentation techniques.
6. Retraining of labelers: Labelers can adapt to the training dataset, which leads to overfitting and poor quality labeling of new data.
Solution: Regularly monitor markup quality and provide feedback to markers. Use cross-validation methods to check the stability and consistency of markups.

Thus, successful markup of textual data requires attention to detail, careful planning, and constant quality control. A combination of automatic and manual labeling methods can greatly improve the process and provide high quality data for model training.

Big Data Science

28 Jul 2023 17:58

📝💡What is CDC: advantages and disadvantages
CDC (Change Data Capture) is a technology for tracking and capturing data changes occurring in a data source, which allows you to efficiently replicate or synchronize data between different systems without the need to completely transfer the entire database. The main goal of CDC is to identify and retrieve only data changes that have occurred since the last capture. This makes the data replication process faster, more efficient, and more scalable.
CDC Benefits:
1. Efficiency of data replication: CDC allows only changed data to be sent, which significantly reduces the amount of data required for synchronization between the data source and the target system. This reduces network load and speeds up the replication process.
2. Scalability: CDC technology is highly scalable, which can handle large amounts of data and high load.
3. Improved Reliability: CDC improves the reliability of the replication system by minimizing the possibility of errors in transmission and forwarding of data.
Disadvantages of CDC:
1. Additional complexity: CDC implementation requires additional configuration and infrastructure, which can increase the complexity of the system and expose it to additional risks of failure.
2. Dependency on the data source: CDC depends on the ability of the data source to capture and provide changed data. If the source does not support CDC, this may be a barrier to its implementation.
3. Data schema conflicts: When synchronizing between systems with different data schemas, conflicts can occur that require additional processing and resolution.
Thus, CDC is a powerful tool for efficient data management and information replication between different systems. However, its successful implementation requires careful planning, tuning and testing to minimize potential risks and ensure reliable system operation.

Big Data Science

20 Jul 2023 17:58

📝💡A few tips for preparing datasets for video analytics
1. Do not require a strict definition to include data in the set: provide a variety of frames so that you can learn about different situations. The higher the diversity of the data set, the better the model will generalize the essence of the detected object.
2. Plan for testing in possible conditions: prepare a list of sites and contacts where you can offer to shoot.
3. Annotate the data: When preparing data for video analytics, it can be useful to annotate the events in the video. This helps to recognize and classify objects more accurately.
4. Time Synchronization: Make sure all cameras are time synchronized. This will help to restore the sequence of events and link the action, which is natural on different cameras.
5. Dividing videos into segments: If you have a search engine for large video files, divide them into smaller segments. This simplifies data processing and analysis, and improves system performance.
6. Video metadata: Create metadata for videos, including timestamps, location information, and other time period data. This is in organizing and searching for video files and events in the subsequent analysis.

Big Data Science

05 Jul 2023 17:59

😎💥YouTube-ASL repository has been made available to the public
This repository provides information about the YouTube-ASL dataset, which is an extensive open source dataset. It contains videos showing American Sign Language with English subtitles.
This dataset includes 11,093 American Sign Language (ASL) footage and has a total length of 984 hours of footage. In addition, there are 610,193 English subtitles in the set.
The repository contains a link to a text document with the video data ID.
This repository is located at the link: https://github.com/google-research/google-research/tree/master/youtube_asl

Big Data Science

28 Jun 2023 17:59

🔎📝Datasets for Natural Language Processing
Sentiment analysis - a set of different datasets, each of which contains the necessary information for analyzing the sentiment of a text. So, the data taken from IMDb is a binary set for sentiment analysis. It consists of 50,000 reviews from the Movie Database (IMDb), marked as either positive or negative.
WikiQA is a collection of question and suggestion pairs. They were collected and annotated to investigate responses to questions in open domains. WikiQA is created using a more natural process. It includes questions for which there are no correct sentences, allowing researchers to work on the response trigger, a critical component of any QA system.
Amazon Reviews dataset - This dataset consists of several million Amazon customer reviews and their ratings. The dataset is used to enable fastText to learn by analyzing consumer sentiment. The idea is that despite the huge amount of data, this is a real business challenge. The model is trained in minutes. This is what sets Amazon Reviews apart from its peers.
Yelp dataset - The Yelp dataset is a collection of businesses, testimonials, and user data that can be applied to a Pet project and academia. You can also use Yelp to teach students how to work with databases, when learning NLP, and as a sample of production data. The dataset is available as JSON files and is a "classic" in natural language processing.
Text classification - Text classification is the task of assigning an appropriate category to a sentence or document. The categories depend on the selected dataset and may vary depending on the topics. For example, TREC is a question classification dataset that consists of fact-based open-ended questions. They are divided into broad semantic categories. The dataset has six-grade (TREC-6) and fifty-grade (TREC-50) versions. Both versions include 5452 training and 500 test cases.

Big Data Science

21 Jun 2023 17:58

⚔️⚡️🤖MDS vs PCA or what is better to use when reducing data dimensionality
Multidimensional Scaling (MDS) and Principal Component Analysis (PCA) are two popular data analysis techniques that are widely used in statistics, machine learning, and data visualization. Both methods aim to compress the information contained in multidimensional data and present it in a form convenient for analysis. Despite similarities in their goals, MDS and PCA have significant differences in their approach and applicability.
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of data. It looks for linear combinations of the original variables, called principal components, that explain the largest amount of variance in the data.
Benefits of PCA:
1. Eliminate multicollinearity: PCA can be used to eliminate multicollinearity inherent in a set of original variables. It allows you to combine related variables into principal components, eliminating information redundancy.
2. Computational speed: PCA is usually quite efficient in terms of computational costs.
3. Resilience to noise: PCA exhibits greater resilience to noise in the data. In PCA, the dominant components (principal components) explain most of the variance in the data, while the less significant components can represent noise. This allows PCA to better separate signals and noise, which is especially useful when analyzing data with a low signal-to-noise ratio.
Disadvantages of PCA:
1. Linear relationship: PCA is based on the assumption of a linear relationship between variables. In the case of a non-linear relationship, PCA may not detect an important data structure.
2. Loss of interpretability: after projecting the data onto the space of principal components, their interpretation can be difficult, since the new variables are linear combinations of the original variables.
3. Sensitivity to outliers: PCA can be sensitive to the presence of outliers in the data, as they can strongly influence the distribution of principal components.

Multidimensional scaling (MDS) is a data visualization technique that seeks to preserve the relative distances between features in the original data when projected into low-dimensional space.
Advantages of MDS:
1. Accounting for non-linear relationships: MDS does not require the assumption of a linear relationship between variables and can detect non-linear relationships in the data.
2. Preserve relative distances: MDS strives to preserve the relative distances between objects in the source data when rendered. This allows you to detect a data structure that may be lost in the process of dimensionality reduction.
3. Interpretability: MDS makes it relatively easy to interpret low-dimensional projections of data because they preserve the relative distances of the original data.
Disadvantages of MDS:
1. Computational Complexity: MDS can be computationally complex when dealing with large datasets, especially when accurate relative distances between all pairs of objects need to be maintained.
2. Dependency on the metric: MDS requires the definition of a distance metric between objects. Choosing the wrong metric can lead to skewed results.

Thus, PCA and MDS are quite effective data analysis tools. PCA is widely used to reduce data dimensionality and reveal structure in linearly dependent variables, while MDS provides the ability to preserve relative distances and detect non-linear relationships between objects. The choice between these methods depends on the specifics of the data and the goals of the analysis. Where dimensionality reduction and principal component detection is required, PCA may be the preferred choice, while MDS is recommended for visualizing and maintaining relative distances in data.

Big Data Science

14 Jun 2023 17:59

🤔🤖Is Trino so good: advantages and disadvantages
Trino is a distributed SQL query engine for large-scale data analysis. It is designed to run analytical queries on large amounts of data that can be distributed across multiple nodes or clusters.
Trino Benefits:
1. Scalable: Trino can handle huge amounts of data efficiently across multiple nodes. It can scale horizontally by adding new nodes to the cluster and load balancing to process requests.
2. High performance with Big Data: Trino is optimized for analytic queries on big data. It uses parallel query processing to speed up complex queries and improve overall performance.
3. Flexibility and compatibility: Trino supports the SQL standard and can work with various data sources such as Hadoop HDFS, Amazon S3, Apache Kafka, MySQL, PostgreSQL and many more. It also integrates with various data analysis tools and platforms.
Trino Disadvantages:
1. Difficult to set up: Setting up and managing the Trino can be a difficult task, especially for non-professionals. It requires skilled professionals to properly tune and optimize performance.
2. Limited support for administrative functions: Trino is focused on executing analytical queries and processing data, so it may have limited support for administrative functions such as monitoring, data backup and recovery. You may need additional tools or settings for these tasks.
3. No built-in resource management system: Trino does not have a built-in resource management system or scheduler. This means that you must use third party tools or tweaks to efficiently allocate resources between queries and control cluster performance.
4. Dependency on Third Party Tools and Platforms: Trino integrates with various data analysis tools and platforms, but its functionality may depend on these third party components. This can make it difficult to manage and update the entire ecosystem, especially when using new versions or additional integrations.
5. Not suitable for transactional operations: Trino is not designed to perform transactional operations such as inserting, updating, and deleting data. If you require transaction processing, you should consider other systems that specialize in this area.

All in all, Trino is a powerful tool for large-scale data analysis, especially in the realm of executing analytical queries on large volumes of data. It provides high performance and flexibility, allows you to work with various data sources and integrate with other data analysis tools and platforms. However, when using Trino, it is necessary to take into account its disadvantages and take into account the specific requirements of the project and infrastructure.

Big Data Science

08 Jun 2023 17:59

⚔️🤖🧠Spark DataFrame vs Pandas Dataframe: Advantages and Disadvantages
Spark DataFrame and Pandas DataFrame are data structures designed to make it easy to work with tabular data in Python, but they have some differences in their functionality and the way they process data.
Spark DataFrame is the core component of Apache Spark, a distributed computing platform for processing large amounts of data. It is a distributed collection of data organized in named columns.
Pandas DataFrame is a data structure provided by the Pandas library that provides powerful tools for parsing and manipulating tabular data. Pandas DataFrame is a two-dimensional labeled array of rows and columns, similar to a database table or spreadsheet.
Benefits of Spark Dataframe:
1. Distributed data processing: Spark Dataframe is designed to process large amounts of data and can work with data that does not fit in the memory of a single node. It distributes data and calculations across the cluster, which allows you to achieve high performance.
2. Programming language support: Spark Dataframe supports multiple programming languages, including Python, Scala, Java, and R. This allows developers to use their preferred language when working with data.
3. Support for different data sources: Spark Dataframe can work with different data sources such as Hadoop Distributed File System (HDFS), Apache Hive, Apache HBase, Apache Cassandra and many more. It provides convenient APIs for working with different data formats.
Disadvantages of Spark Dataframe:
1. Difficulty in setting up and managing a cluster: Spark requires setting up and managing a cluster for distributed data processing. This can be difficult for first time users or projects with limited resources.
2. Slow startup: Starting a Spark cluster can take time, especially if networking and other settings need to be configured. For small datasets, this can be inefficient and take longer than processing the data by Spark itself.

Benefits of Pandas Dataframe:
1. Ease of use: Pandas Dataframe provides a simple and intuitive API for working with data. It offers many features for filtering, sorting, grouping, and aggregating data, making it convenient for data analysis.
2. Large user community: Pandas is a very popular tool in the data analytics and machine learning community. This means that there are many resources, documentation and communities where you can get help and support.
3. High performance on small datasets: Pandas is optimized to work with relatively small datasets that can fit in the memory of a single node. In such cases, Pandas can be faster than Spark.
Disadvantages of Pandas Dataframe:
1. Memory Limits: Pandas Dataframe stores all data in memory, so working with large datasets can be limited by the available memory on your computer. This can cause performance issues or even crash the program.
2. Limited scalability: Pandas is designed to run on a single node and cannot scale efficiently for distributed processing. If you have a large amount of data that doesn't fit in a single node's memory, Pandas can become inefficient.
Thus, the choice between Spark Dataframe and Pandas Dataframe depends on specific needs. If there are large amounts of data and distributed processing is required, then Spark Dataframe may be preferable. If you are working with small datasets and looking for a simple and fast way to analyze your data, then Pandas Dataframe might be the best choice.