Unstructured Data Analytics Market Outlook

According to our latest research, the global unstructured data analytics market size reached USD 10.4 billion in 2024, reflecting robust demand across industries seeking actionable insights from vast volumes of unstructured data. The market is expected to grow at a remarkable CAGR of 22.7% from 2025 to 2033, reaching a projected size of USD 80.2 billion by 2033. This exceptional growth is primarily driven by the exponential increase in data generation, the proliferation of advanced analytics and artificial intelligence technologies, and the urgent need for organizations to derive value from data sources such as emails, social media, documents, and multimedia files.

One of the most significant growth factors propelling the unstructured data analytics market is the sheer volume of unstructured data generated daily from diverse digital channels. As enterprises continue their digital transformation journeys, they accumulate vast amounts of data that do not fit neatly into traditional databases. This includes customer interactions on social media, multimedia content, sensor data, and more. The inability to harness this data can lead to missed opportunities and competitive disadvantages. As a result, organizations across sectors are investing heavily in unstructured data analytics solutions to unlock hidden patterns, enhance decision-making, and drive innovation. The rapid adoption of Internet of Things (IoT) devices and the expansion of digital business models further amplify the need for advanced analytics platforms capable of handling complex, unstructured information.

Another critical driver for market expansion is the integration of artificial intelligence (AI) and machine learning (ML) technologies within unstructured data analytics platforms. These technologies enable organizations to process, analyze, and interpret vast datasets with unprecedented speed and accuracy. Natural language processing (NLP), image recognition, and sentiment analysis are just a few examples of AI-driven capabilities that are transforming how businesses extract insights from unstructured data. The growing sophistication of these tools allows companies to automate labor-intensive processes, reduce operational costs, and gain real-time visibility into market trends and customer sentiments. As AI and ML continue to evolve, their integration into unstructured data analytics solutions is expected to further accelerate market growth and adoption across all major industries.

The increasing emphasis on regulatory compliance and risk management is also fueling the adoption of unstructured data analytics. Regulatory bodies worldwide are enforcing stricter data governance and privacy regulations, compelling organizations to monitor and analyze all forms of data, including unstructured content. Failure to comply with these regulations can result in significant financial penalties and reputational damage. Advanced analytics solutions empower businesses to proactively identify compliance risks, detect fraudulent activities, and ensure adherence to industry standards. This regulatory landscape, combined with the strategic benefits of data-driven insights, is prompting organizations in sectors such as BFSI, healthcare, and government to prioritize investments in unstructured data analytics.

From a regional perspective, North America currently dominates the unstructured data analytics market, accounting for the largest revenue share in 2024 due to the high concentration of technology-driven enterprises and early adoption of advanced analytics solutions. However, the Asia Pacific region is poised for the fastest growth during the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing investments in AI and big data analytics. Europe also represents a significant market, supported by strong regulatory frameworks and a focus on data-driven business strategies. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual adoption, with growing awareness of the strategic value of unstructured data analytics in improving operational efficiency and customer engagement.

Dataset

This dataset was created by ajacks

Released under Apache 2.0

Contents

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

Unstructured Data Governance Market Outlook

According to our latest research, the global unstructured data governance market size reached USD 3.2 billion in 2024, reflecting the rapid adoption of data governance solutions across organizations worldwide. The market is set to expand at a robust CAGR of 21.4% during the forecast period, with the total value projected to reach USD 22.1 billion by 2033. This remarkable growth is primarily driven by escalating data volumes, increasing regulatory scrutiny, and the urgent need for enterprises to extract actionable insights from unstructured information sources.

The primary growth factor for the unstructured data governance market is the exponential surge in data generation driven by digital transformation initiatives, IoT proliferation, and the widespread adoption of cloud technologies. Organizations are inundated with vast amounts of unstructured data, such as emails, documents, images, videos, and social media content, which often remains untapped or poorly managed. As businesses recognize the strategic value of this data for decision-making, customer engagement, and innovation, the demand for robust governance frameworks and advanced analytical tools has intensified. Moreover, the shift toward hybrid and multi-cloud environments has made data management more complex, necessitating sophisticated governance solutions that can seamlessly handle unstructured data across disparate sources.

Another significant driver propelling the unstructured data governance market is the tightening regulatory landscape. Regulatory bodies worldwide, including GDPR in Europe, CCPA in California, and other data privacy laws, are imposing stringent requirements on data management, privacy, and security. Non-compliance can result in hefty fines, reputational damage, and legal liabilities. Consequently, organizations are prioritizing investments in governance solutions that ensure data lineage, classification, access controls, and auditability, specifically for unstructured data assets. Additionally, the rising frequency and sophistication of cyber threats have heightened awareness around data security, further fueling the adoption of governance frameworks that safeguard sensitive information and mitigate risks.

Technological advancements are also reshaping the unstructured data governance market landscape. Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are being integrated into governance solutions to automate data discovery, classification, and policy enforcement. These technologies enable organizations to efficiently manage massive volumes of unstructured data, identify sensitive information, and detect anomalies in real-time. Furthermore, the growing emphasis on data quality, integration, and interoperability across business units is driving the need for comprehensive governance platforms that provide holistic visibility and control. As digital ecosystems become more interconnected, the ability to govern unstructured data effectively is becoming a critical competitive differentiator.

From a regional perspective, North America currently leads the unstructured data governance market, accounting for the largest revenue share in 2024. This dominance can be attributed to the presence of major technology vendors, early adoption of advanced data management solutions, and a mature regulatory environment. Europe follows closely, driven by strict data privacy regulations and increasing investments in digital infrastructure. The Asia Pacific region is poised for the fastest growth, fueled by rapid digitalization, expanding enterprise IT budgets, and the emergence of data-driven business models across various industries. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual adoption, with market growth supported by government initiatives and increasing awareness of data governance benefits.

Component Analysis

The unstructured data governance market is segmented by component into solutions and service

Global Unstructured Data Solution market size 2025 was XX Million. Unstructured Data Solution Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Unstructured Data Classification Market Outlook

According to our latest research, the global unstructured data classification market size reached USD 2.31 billion in 2024, reflecting robust demand across sectors. The market is anticipated to grow at a CAGR of 22.8% from 2025 to 2033, with the market size projected to reach USD 17.3 billion by 2033. This remarkable growth is primarily driven by the exponential increase in unstructured data generation, alongside heightened requirements for data security, compliance, and intelligent information management solutions.

The primary growth driver for the unstructured data classification market is the rapid proliferation of data from diverse sources such as emails, social media, IoT devices, and multimedia content. Organizations globally are witnessing a data deluge, with over 80% of enterprise data estimated to be unstructured. This surge has created an urgent need for advanced classification solutions that can efficiently process, categorize, and extract actionable insights from vast volumes of data. Furthermore, the integration of artificial intelligence and machine learning algorithms has significantly enhanced the accuracy and scalability of unstructured data classification, making these solutions indispensable for modern enterprises seeking to optimize operations and extract value from their data assets.

Another significant growth factor is the evolving regulatory landscape that mandates stringent data governance and compliance. With regulations like GDPR, CCPA, and industry-specific standards, businesses are compelled to implement robust data classification frameworks to ensure sensitive information is properly identified, protected, and managed. This has led to increased investments in unstructured data classification solutions, particularly in highly regulated industries such as BFSI, healthcare, and government. Additionally, the rising threat of data breaches and cyberattacks has heightened the focus on data security, further fueling the adoption of classification tools that can proactively identify and safeguard critical information.

The digital transformation wave sweeping across industries is also propelling the market forward. Enterprises are increasingly adopting cloud-based platforms, remote work models, and digital collaboration tools, all of which contribute to the exponential growth of unstructured data. As organizations strive for improved operational efficiency and agility, the demand for scalable and automated data classification solutions is set to escalate. Additionally, the emergence of big data analytics and the growing focus on deriving business intelligence from unstructured sources are expected to provide significant impetus to market expansion over the forecast period.

Regionally, North America continues to dominate the unstructured data classification market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the presence of major technology providers, advanced IT infrastructure, and high regulatory awareness. However, Asia Pacific is expected to witness the fastest growth rate, driven by rapid digitalization, increasing cloud adoption, and expanding investments in data security initiatives. Europe also holds a substantial market share, bolstered by stringent data privacy regulations and a mature enterprise landscape. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing awareness and adoption of data management solutions.

Component Analysis

The unstructured data classification market by component is segmented into software and services. Software solutions constitute the backbone of this market, offering advanced tools for automated data discovery, classification, and management. The software segment has seen significant innovation, with vendors integrating AI, NLP, and deep learning technologies to improve the accuracy and efficiency of data classification

Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled instances). In this article, we introduce a novel generative method, called a coupled generator, producing instances given a specific learning outcome, based on indirect and direct generators. The indirect generator uses the inverse principle to yield the corresponding inverse probability, enabling to generate instances by leveraging an unlabeled data. The direct generator learns the distribution of an instance given its learning outcome. Then, the coupled generator seeks the best one from the indirect and direct generators, which is designed to enjoy the benefits of both and deliver higher generation accuracy. For sentence generation given a topic, we develop an embedding-based regression/classification in conjuncture with an unconditional recurrent neural network for the indirect generator, whereas a conditional recurrent neural network is natural for the corresponding direct generator. Moreover, we derive finite-sample generation error bounds for the indirect and direct generators to reveal the generative aspects of both methods thus explaining the benefits of the coupled generator. Finally, we apply the proposed methods to a real benchmark of abstract classification and demonstrate that the coupled generator composes reasonably good sentences from a dictionary to describe a specific topic of interest.

NoSQL Software Market Outlook

The global NoSQL software market size was valued at approximately USD 6 billion in 2023 and is projected to reach around USD 20 billion by 2032, growing at a compound annual growth rate (CAGR) of 14% during the forecast period. This market is driven by the escalating need for operational efficiency, flexibility, and scalability in database management systems, particularly in enterprises dealing with vast amounts of unstructured data.

One of the primary growth factors propelling the NoSQL software market is the exponential increase in data volumes generated by various digital platforms, IoT devices, and social media. Traditional relational databases often struggle to handle this surge efficiently, prompting organizations to shift towards NoSQL databases that offer more flexibility and scalability. The ability to store and process large sets of unstructured data without needing a predefined schema makes NoSQL databases an attractive choice for modern businesses seeking agility and speed in data management.

Moreover, the proliferation of cloud computing services has significantly contributed to the growth of the NoSQL software market. Cloud-based NoSQL databases provide cost-effective, scalable, and easily accessible solutions for enterprises of all sizes. The pay-as-you-go pricing model and the capacity to scale resources based on demand have made NoSQL databases a preferred option for startups and large enterprises alike. The seamless integration of NoSQL databases with cloud infrastructure enhances operational efficiencies and reduces the complexities associated with database management.

Another critical driver is the increasing adoption of NoSQL databases in various industry verticals such as retail, BFSI, IT, and healthcare. These industries require robust data management solutions to handle large volumes of diverse data types. NoSQL databases, with their flexible data models and high performance, cater to these requirements efficiently. In the retail sector, for example, NoSQL databases are used to manage customer data, product catalogs, and transaction histories, enabling more personalized and efficient customer services.

Regionally, North America holds a significant share of the NoSQL software market due to the presence of major technology companies and a mature IT infrastructure. The rapid digital transformation across enterprises in the region, alongside substantial investments in big data analytics and cloud computing, further fuels market growth. Additionally, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the expanding IT sector, increased adoption of cloud services, and significant investments in digital technologies in countries like China and India.

Graph Databases Software has emerged as a crucial component in the landscape of NoSQL databases, particularly for applications that require understanding complex relationships between data entities. Unlike traditional databases that store data in tables, graph databases use nodes, edges, and properties to represent and store data, making them ideal for scenarios where relationships are as important as the data itself. This approach is particularly beneficial in fields such as social networking, where the ability to analyze connections between users can provide deep insights into social dynamics and influence patterns. As businesses increasingly seek to leverage data for competitive advantage, the demand for graph databases is expected to grow, driven by their ability to efficiently model and query interconnected data.

Type Analysis

The NoSQL software market is segmented into various types, including Document-Oriented, Key-Value Store, Column-Oriented, and Graph-Based databases. Document-oriented databases, such as MongoDB, store data in JSON-like documents, offering flexibility in data modeling and ease of use. These databases are widely used for content management systems, e-commerce applications, and real-time analytics. Their ability to handle semi-structured data and scalability features make them a popular choice among developers and enterprises seeking agile database solutions.

Key-Value Store databases, such as Redis and Amazon DynamoDB, store data as a collection of key-value pairs, providing ultra-fast read and write operations. These databases are ideal for applications requiring high-speed data retrieval, such as caching, session manag

Identifying Entities (Diseases, Treatments) in Healthcare Data

Finding diseases and treatments in medical text—because even AI needs a medical degree to understand doctor’s notes! 🩺🤖

📊 Understanding the Dataset

In the contemporary healthcare ecosystem, substantial amounts of unstructured textual facts are generated day by day thru electronic health facts (EHRs), medical doctor’s notes, prescriptions, and medical literature. The potential to extract meaningful insights from this records is critical for improving patient care, advancing clinical studies, and optimizing healthcare offerings. The dataset in cognizance incorporates text-based totally scientific statistics, in which sicknesses and their corresponding remedies are embedded inside unstructured sentences.

The dataset consists of categorized textual content samples, that are classified into: -**Train Sentences**: These sentences comprise clinical records, including patient diagnoses and the treatments administered. -**Train Labels**: The corresponding annotations for the train sentences, marking diseases and remedies as named entities. -**Test Sentences**: Similar to educate sentences however used to evaluate model overall performance. -**Test Labels**: The ground reality labels for the test sentences.

A sneak from the dataset may look as follows:

🔍 Example from Dataset:

Train Sentences:

_ "The patient was a 62 -year -old man with squamous epithelium, who was previously treated with success with a combination of radiation therapy and chemotherapy."

Train Labels:

• Disease: 🦠 lung cancer
• Treatment: 💉 Radiation therapy, chemotherapy

This dataset requires the use of** designated Unit Recognition (NER)** to remove and map and map diseases for related treatments 💊, causing the composition of unarmed medical data for analytical purposes.

⚙️ Dataset Properties

1. Unnecessary medical text: Data set contains free-powered medical notes, where disease and treatment conditions are clearly mentioned. Removing this information without clear mapping is a challenge.
2. Many unit types: Datasets contain different - -called institutions such as diseases, treatment, symptoms and possibly medication.
3. Relevant addiction: Many treatments apply to many diseases, and proper mapping depends on reference. For example, "radiotherapy" is used for different cancers, which makes relevant understanding significantly.
4. Unbalanced data distribution: Some diseases and treatment can be displayed more often than others, to balance model performance requires techniques such as overfalling, sub -sampling or transmission of learning.
5. Domain-specific language: is rich in lesson medical terminology, which requires special preprochet using domain-specific NLP techniques and medical oncology such as UML or SNOM CT.

🚧 Challenges Working with Dataset

• Complex medical vocabulary: Medical texts often use vocals, which require special NLP models that are trained at the clinical company.

• Implicit Relationships: Unlike based datasets, ailment-treatment relationships are inferred from context in preference to explicitly stated.

• Synonyms and Abbreviations: Diseases and treatments can be cited the use of special names (e.G., ‘myocardial infarction’ vs. ‘coronary heart assault’). Handling such versions is vital.

• Noise in Data: Unstructured records may additionally contain irrelevant records, typographical errors, and inconsistencies that affect extraction accuracy.

🛠️ Approach to Extracting Insights from the Dataset

To extract sicknesses and their respective treatments from this dataset, we follow a based NLP pipeline:

1. Data Preprocessing 🧹

• Text Cleaning: Remove needless characters, numbers, and stopwords whilst preserving clinical terms.
• Tokenization: Split sentences into phrases for higher processing.
• Medical Term Standardization: Use area-precise libraries like SciSpacy to standardize synonyms and abbreviations.

2. Named Entity Recognition (NER) Model Development 🤖

• Annotation: Ensure accurate labeling of sicknesses and treatments in the dataset.
• Model Selection: Train a deep-mastering-based version like BioBERT or a rule-based model the use of spaCy.
• Training: Use annotated data to teach a custom NER model that classifies words as sickness or treatment entities.
• Evaluation: Measure precision, bear in mind, and F1-score to evaluate version overall performance.

3. Mapping Diseases to Treatments 🔄

• Contextual Relationship Extraction: Identify which treatment corresponds to which sickness using dependency parsing and courting extraction.
• Dictionary or Tabular Output: Store extracted mappings in a based layout.

Example Output:

| 🦠 Disease | 💉 Treatments | |----------|--------------------...

With this collection of code and configuration files (contained in "LMIF" = 'Learned Metric Index Framework'), outputs ("output-files") and datasets ("data") we set out to explore whether a learned approach to building a metric index is a viable alternative to the traditional way of constructing metric indexes. Specifically, we build the index as a series of interconnected machine learning models. This collection serves as the basis for the reproducibility paper accompanying our parent paper -- "Learned metric index—proposition of learned indexing for unstructured data" [1].

1. In "data" we make publicly available a collection of 3 individual dataset descriptors -- CoPhIR (1 million objects, 282 columns), Profimedia (1 million objects, 4096 columns), and MoCap (~350k objects, 4096 columns), "labels" obtained from a template index -- M-tree or M-index, "queries" used to perform an experimental search with and "ground-truths" to evaluate the approximate k-NN performance of the index. Within "test" we include dummy data to ease the integration of any custom dataset (examples in "LMIF/*.ipynb") that a reader may want to integrate into our solution. In CoPhIR [2], each of the vectors is obtained by concatenating five MPEG-7 global visual descriptors extracted from an image downloaded from Flickr. The Profimedia image dataset [3], contains Caffe visual descriptors extracted from Photo-stock images by a convolutional neural network. MoCap (motion capture data) [4] descriptors contain sequences of 3D skeleton poses extracted from 3+ hrs of recordings capturing actors performing more than 70 different motion scenarios. The dataset's size is 43 GB upon decompression.

[1] Antol, Matej, et al. "Learned metric index—proposition of learned indexing for unstructured data." Information Systems 100 (2021): 101774. [2] Batko, Michal, et al. "Building a web-scale image similarity search system." Multimedia Tools and Applications 47.3 (2010): 599-629. [3] Budikova, Petra et al. "Evaluation platform for content-based image retrieval systems." International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 2011. [4] Müller, Meinard, et al. "Documentation mocap database hdm05." (2007).

1. "LMIF" contains a user-friendly environment to reproduce the experiments in [1]. LMIF consists of three components:
2. an implementation of the Learned Metric Index (distributed under the MIT license),
3. a collection of scripts and configuration setups necessary for re-running the experiments in [1] and

4. instructions for creating the reproducibility environment (Docker). For a thorough description of "LMIF", please refer to our reproducibility paper -- "Reproducible experiments on Learned Metric Index – proposition of learned indexing for unstructured data".

5. "output-files" contain the reproduced outputs for each experiment, with generated figures and a concise ".html" report (as presented in [1])

Unstructured Data Management Platform Market Outlook

As per our latest research, the global unstructured data management platform market size reached USD 12.7 billion in 2024, with a robust year-on-year expansion driven by the exponential growth of digital data. The market is projected to grow at a CAGR of 14.2% from 2025 to 2033, reaching an estimated USD 39.8 billion by 2033. This remarkable growth trajectory is primarily attributed to the increasing adoption of advanced analytics, artificial intelligence, and cloud computing technologies that necessitate sophisticated management of unstructured data across diverse industry verticals.

The surge in unstructured data management platform market growth is fueled by the proliferation of digital transformation initiatives across enterprises globally. Organizations are generating vast volumes of unstructured data from sources such as emails, social media, IoT devices, audio, video, and documents. The need to extract actionable insights from this data to drive business intelligence, enhance customer experiences, and optimize operations is pushing enterprises to adopt advanced unstructured data management platforms. Furthermore, the rise of big data analytics and AI-driven decision-making processes has made it imperative for businesses to manage, process, and analyze unstructured data efficiently. This trend is particularly pronounced in sectors like healthcare, BFSI, and retail, where data-driven strategies are critical for competitive differentiation and regulatory compliance.

Another significant growth factor for the unstructured data management platform market is the increasing focus on regulatory compliance and data security. With stringent data protection regulations such as GDPR, HIPAA, and CCPA being enforced globally, organizations are under pressure to ensure proper governance of all data types, including unstructured data. Unstructured data management platforms offer robust data governance, classification, and auditing capabilities, enabling organizations to adhere to regulatory mandates while minimizing risks associated with data breaches and non-compliance. The growing awareness of the legal and financial implications of data mismanagement is prompting enterprises to invest in comprehensive unstructured data management solutions that guarantee data integrity, traceability, and secure access.

The accelerating shift towards cloud-based infrastructure and hybrid IT environments is also a major catalyst for the growth of the unstructured data management platform market. As organizations migrate workloads to the cloud and adopt multi-cloud strategies, managing unstructured data across disparate environments becomes increasingly complex. Unstructured data management platforms provide the scalability, flexibility, and centralized control needed to manage data seamlessly across on-premises and cloud platforms. This is particularly beneficial for large enterprises with global operations, as well as for small and medium-sized enterprises seeking cost-effective data management solutions. The integration of AI and machine learning capabilities within these platforms further enhances their value proposition, enabling automated data classification, anomaly detection, and predictive analytics.

From a regional perspective, North America continues to dominate the unstructured data management platform market, accounting for the largest revenue share in 2024. This leadership position is attributed to the early adoption of digital technologies, a mature IT ecosystem, and significant investments in data-driven innovation. Europe and Asia Pacific are also witnessing substantial growth, driven by increasing digitalization, expanding regulatory frameworks, and the rising adoption of cloud services. The Asia Pacific region, in particular, is expected to register the highest CAGR during the forecast period, fueled by rapid economic development, a burgeoning startup ecosystem, and government initiatives promoting digital transformation across various sectors.

Component Analysis

Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to synthesize high-fidelity data for improving model performance across multimodal domains, including tabular, textual, and image data. CoDSA generates synthetic samples that faithfully capture the conditional distributions of the original data, with a focus on under-sampled or high-interest regions. Through transfer learning, CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas. This process preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and boosts generalization. We also introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation, providing formal guarantees of its effectiveness. Extensive experiments demonstrate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings.

The "Wikipedia SQLite Portable DB" is a compact and efficient database derived from the Kensho Derived Wikimedia Dataset (KDWD). This dataset provides a condensed subset of raw Wikimedia data in a format optimized for natural language processing (NLP) research and applications.

I am not affiliated or partnered with the Kensho in any way, just really like the dataset for giving my agents to query easily.

Key Features:

Contains over 5 million rows of data from English Wikipedia and Wikidata Stored in a portable SQLite database format for easy integration and querying Includes a link-annotated corpus of English Wikipedia pages and a compact sample of the Wikidata knowledge base Ideal for NLP tasks, machine learning, data analysis, and research projects

The database consists of four main tables:

• items: Contains information about Wikipedia items, including labels and descriptions
• properties: Stores details about Wikidata properties, such as labels and descriptions
• pages: Provides metadata for Wikipedia pages, including page IDs, item IDs, titles, and view counts
• link_annotated_text: Contains the link-annotated text of Wikipedia pages, divided into sections

This dataset is derived from the Kensho Derived Wikimedia Dataset (KDWD), which is built from the English Wikipedia snapshot from December 1, 2019, and the Wikidata snapshot from December 2, 2019. The KDWD is a condensed subset of the raw Wikimedia data in a form that is helpful for NLP work, and it is released under the CC BY-SA 3.0 license. Credits: The "Wikipedia SQLite Portable DB" is derived from the Kensho Derived Wikimedia Dataset (KDWD), created by the Kensho R&D group. The KDWD is based on data from Wikipedia and Wikidata, which are crowd-sourced projects supported by the Wikimedia Foundation. We would like to acknowledge and thank the Kensho R&D group for their efforts in creating the KDWD and making it available for research and development purposes. By providing this portable SQLite database, we aim to make Wikipedia data more accessible and easier to use for researchers, data scientists, and developers working on NLP tasks, machine learning projects, and other data-driven applications. We hope that this dataset will contribute to the advancement of NLP research and the development of innovative applications utilizing Wikipedia data.

https://www.kaggle.com/datasets/kenshoresearch/kensho-derived-wikimedia-data/data

Tags: encyclopedia, wikipedia, sqlite, database, reference, knowledge-base, articles, information-retrieval, natural-language-processing, nlp, text-data, large-dataset, multi-table, data-science, machine-learning, research, data-analysis, data-mining, content-analysis, information-extraction, text-mining, text-classification, topic-modeling, language-modeling, question-answering, fact-checking, entity-recognition, named-entity-recognition, link-prediction, graph-analysis, network-analysis, knowledge-graph, ontology, semantic-web, structured-data, unstructured-data, data-integration, data-processing, data-cleaning, data-wrangling, data-visualization, exploratory-data-analysis, eda, corpus, document-collection, open-source, crowdsourced, collaborative, online-encyclopedia, web-data, hyperlinks, categories, page-views, page-links, embeddings

Usage with LIKE queries: ``` import aiosqlite import asyncio

class KenshoDatasetQuery: def init(self, db_file): self.db_file = db_file

async def _aenter_(self):
  self.conn = await aiosqlite.connect(self.db_file)
  return self

async def _aexit_(self, exc_type, exc_val, exc_tb):
  await self.conn.close()

async def search_pages_by_title(self, title):
  query = """
  SELECT pages.page_id, pages.item_id, pages.title, pages.views, 
      items.labels AS item_labels, items.description AS item_description,
      link_annotated_text.sections
  FROM pages 
  JOIN items ON pages.item_id = items.id
  JOIN link_annotated_text ON pages.page_id = link_annotated_text.page_id
  WHERE pages.title LIKE ?
  """
  async with self.conn.execute(query, (f"%{title}%",)) as cursor:
    return await cursor.fetchall()

async def search_items_by_label_or_description(self, keyword):
  query = """
  SELECT id, labels, description 
  FROM items
  WHERE labels LIKE ? OR description LIKE ?
  """
  async with self.conn.execute(query, (f"%{keyword}%", f"%{keyword}%")) as cursor:
    return await cursor.fetchall()

async def search_items_by_label(self, label):
  query = """
  SELECT id, labels, description
  FROM items 
  WHERE labels LIKE ?
  """
  async with self.conn.execute(query, (f"%{label}%",)) as cursor:
    return await cursor.fetchall()

async def search_properties_by_label_or_desc...

Unstructured Data Entitlement Management Market Outlook

According to our latest research, the global unstructured data entitlement management market size reached USD 3.6 billion in 2024, reflecting a robust expansion driven by the proliferation of digital data across industries. The market is expected to grow at a CAGR of 16.9% from 2025 to 2033, with the forecasted market size projected to hit USD 15.2 billion by 2033. This remarkable growth is primarily fueled by the increasing need for robust data governance, stringent regulatory compliance requirements, and the rising risks associated with unauthorized data access in enterprises worldwide.

The exponential surge in unstructured data—ranging from emails, documents, images, to multimedia content—has become a critical challenge for organizations. As businesses digitize operations and embrace hybrid work environments, the volume of unstructured data continues to rise, necessitating advanced entitlement management solutions. These solutions provide granular access controls, ensuring that only authorized personnel can access sensitive information. The growing adoption of cloud storage and collaboration platforms further amplifies the need for sophisticated entitlement management tools that can adapt to dynamic data environments and complex user hierarchies. Additionally, the integration of artificial intelligence and machine learning capabilities into these solutions is enhancing their ability to identify, classify, and manage access to unstructured data in real time, thereby reducing the risk of data breaches and insider threats.

Another significant growth driver is the tightening regulatory landscape across various sectors. Organizations in industries such as BFSI, healthcare, and government are increasingly mandated to comply with data protection regulations such as GDPR, HIPAA, and CCPA. These regulations require enterprises to implement stringent controls over who can access specific data sets and to maintain detailed audit trails for compliance reporting. Unstructured data entitlement management solutions are essential in helping organizations automate compliance processes, enforce least-privilege policies, and demonstrate accountability during audits. As regulatory scrutiny intensifies, particularly around data privacy and security, the demand for these solutions is expected to accelerate further, driving sustained market expansion over the forecast period.

The shift towards remote and hybrid work models has also contributed to the growth of the unstructured data entitlement management market. With employees accessing corporate data from diverse locations and devices, the traditional perimeter-based security models are no longer sufficient. Organizations are increasingly adopting zero-trust security frameworks, wherein entitlement management plays a pivotal role in enforcing access policies based on user identity, context, and data sensitivity. This paradigm shift is compelling enterprises to invest in scalable, cloud-native entitlement management platforms that can seamlessly integrate with existing IT ecosystems while providing centralized visibility and control over unstructured data access. The ongoing digital transformation initiatives across industries are expected to further bolster market growth, as organizations prioritize data security and governance in their technology strategies.

Regionally, North America is expected to maintain its dominance in the global unstructured data entitlement management market, owing to the presence of a large number of technology-driven enterprises, stringent regulatory frameworks, and early adoption of advanced security solutions. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by rapid digitalization, increasing awareness about data security, and rising investments in IT infrastructure across emerging economies such as China and India. Europe is also projected to experience significant growth, supported by robust data protection regulations and the proliferation of cloud-based services. Latin America and the Middle East & Africa are gradually catching up, as organizations in these regions recognize the importance of data entitlement management in mitigating cyber risks and ensuring regulatory compliance.

According to Cognitive Market Research, the global object storage market size was USD 6124.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 10.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 2449.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1837.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1408.57 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 306.21 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 122.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.9% from 2024 to 2031.
The cloud category is the fastest growing segment of the object storage industry

Market Dynamics of Object Storage Market

Key Drivers for Object Storage Market

Growing Adoption of Hybrid Cloud Architectures to Boost Market Growth

The growing adoption of hybrid cloud architectures is fueling the expansion of the object storage market. Hybrid cloud environments, which combine on-premises and cloud resources, offer flexibility and scalability for managing large volumes of unstructured data. Object storage, with its scalable, cost-efficient, and cloud-native architecture, is ideally suited for hybrid clouds, enabling organizations to store data seamlessly across multiple environments. This trend is driven by the need for better data accessibility, disaster recovery, and the integration of cloud storage into traditional enterprise IT systems, further boosting object storage demand. For instance, in January 2024, Quantum Corporation declared that Amidata had implemented Quantum ActiveScale object storage as the foundation for their recent Amidata Secure Cloud Storage Service. After building a successful Backup-as-a-Service and File Sharing Service delivering on Quantum DXi™ backup appliances and Quantum StorNext® file systems, Amidata has now deployed ActiveScale object storage to create a secure, resilient set of cloud storage services accessible from across all of Australia, where the firm is based.

Advancements in Technology to Drive Market Growth

Advancements in technology are significantly driving growth in the object storage market. Innovations such as AI-powered data management, improved scalability, and better integration with cloud-native architectures are enhancing object storage's appeal for handling massive unstructured data. The rise of edge computing and hybrid cloud models further boosts the demand for object storage, providing seamless data access across distributed environments. Enhanced security features, such as encryption and data immutability, are addressing security concerns, making object storage an attractive option for industries requiring scalable, durable, and secure data storage solutions.

Key Restraints for Object Storage Market

Complex Integration with Legacy Systems will Limit Market Growth

A significant restraint in the object storage market is the complex integration with legacy systems. Many organizations rely on traditional storage infrastructure (like block and file storage), and transitioning to object storage can be challenging. Legacy systems are often not designed to interface with modern object-based architectures, leading to compatibility issues and requiring complex re-engineering. This process can be time-consuming and costly, making businesses hesitant to adopt object storage solutions. As a result, this challenge slows down market adoption, particularly for established enterprises with deeply entrenched legacy systems.

Key Trends for Object Storage Market

The object storage industry is expanding due to scalability, AI integration, and hybrid cloud acceptance.

The increasing demand for scalable, cost-effective solutions to handle exponential data expansion, notably from AI/ML workloads, IoT devices, and unstructured data, is a significant trend in the market for object stora...

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Text Analytics Market Size 2024-2028

The text analytics market size is forecast to increase by USD 18.08 billion, at a CAGR of 22.58% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of Service-Oriented Architecture (SOA) among end-users. SOA's flexibility and scalability make it an ideal choice for text analytics applications, enabling organizations to process vast amounts of unstructured data and gain valuable insights. Additionally, the ability to analyze large volumes of unstructured data provides valuable insights through data analytics, enabling informed decision-making and competitive advantage. Furthermore, the emergence of advanced text analytical tools is expanding the market's potential by offering enhanced capabilities, such as sentiment analysis, entity extraction, and topic modeling. However, the market faces challenges that require careful consideration. System integration and interoperability issues persist, as text analytics solutions must seamlessly integrate with existing IT infrastructure and data sources.
Ensuring compatibility and data exchange between various systems can be a complex and time-consuming process. Addressing these challenges through strategic partnerships, standardization efforts, and open APIs will be essential for market participants to capitalize on the opportunities presented by the market's growth.

What will be the Size of the Text Analytics Market during the forecast period?

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The market continues to evolve, driven by advancements in technology and the increasing demand for insightful data interpretation across various sectors. Text preprocessing techniques, such as stop word removal and lexical analysis, form the foundation of text analytics, enabling the extraction of meaningful insights from unstructured data. Topic modeling and transformer networks are current trends, offering improved accuracy and efficiency in identifying patterns and relationships within large volumes of text data. Applications of text analytics extend to fake news detection, risk management, and brand monitoring, among others. Data mining, customer feedback analysis, and data governance are essential components of text analytics, ensuring data security and maintaining data quality.

Text summarization, named entity recognition, deep learning, and predictive modeling are advanced techniques that enhance the capabilities of text analytics, providing actionable insights through data interpretation and data visualization. Machine learning algorithms, including machine learning and deep learning, play a crucial role in text analytics, with applications in spam detection, sentiment analysis, and predictive modeling. Syntactic analysis and semantic analysis offer deeper understanding of text data, while algorithm efficiency and performance optimization ensure the scalability of text analytics solutions. Text analytics continues to unfold, with ongoing research and development in areas such as prescriptive modeling, API integration, and data cleaning, further expanding its applications and capabilities.

The future of text analytics lies in its ability to provide valuable insights from unstructured data, driving informed decision-making and business growth.

How is this Text Analytics Industry segmented?

The text analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premises


Component

  Software
  Services


Geography

  North America

    US


  Europe

    France
    Germany


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Text analytics is a dynamic and evolving market, driven by the increasing importance of data-driven insights for businesses. Cloud computing plays a significant role in its growth, as companies such as Microsoft, SAP SE, SAS Institute, IBM, Lexalytics, and Open Text offer text analytics software and services via the Software-as-a-Service (SaaS) model. This approach reduces upfront costs for end-users, as they do not need to install hardware and software on their premises. Instead, these solutions are maintained at the company's data center, allowing end-users to access them on a subscription basis. Text preprocessing, topic modeling, transformer networks, and other advanced techniques are integral to text analytics.

Fake news detection, spam filtering, sentiment analysis, and social media monitoring are essential applications. Deep learning, machine l

As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more desirable to analyze both structured data records and unstructured text data simultaneously. Although online analytical processing (OLAP) techniques have been proven very useful for analyzing and mining structured data, they face challenges in handling text data. On the other hand, probabilistic topic models are among the most effective approaches to latent topic analysis and mining on text data. In this paper, we study a new data model called topic cube to combine OLAP with probabilistic topic modeling and enable OLAP on the dimension of text data in a multidimensional text database. Topic cube extends the traditional data cube to cope with a topic hierarchy and stores probabilistic content measures of text documents learned through a probabilistic topic model. To materialize topic cubes efficiently, we propose two heuristic aggregations to speed up the iterative Expectation-Maximization (EM) algorithm for estimating topic models by leveraging the models learned on component data cells to choose a good starting point for iteration. Experimental results show that these heuristic aggregations are much faster than the baseline method of computing each topic cube from scratch. We also discuss some potential uses of topic cube and show sample experimental results.

Relation extraction (RE) is concerned with developing methods and models that automatically detect and retrieve relational information from unstructured data. It is crucial to information extraction (IE) applications that aim to leverage the vast amount of knowledge contained in unstructured natural language text, for example, in web pages, online news, and social media; and simultaneously require the powerful and clean semantics of structured databases instead of searching, querying, and analyzing unstructured text directly. In practical applications, however, relation extraction is often characterized by limited availability of labeled data, due to the cost of annotation or scarcity of domain-specific resources. In such scenarios it is difficult to create models that perform well on the task. It therefore is desired to develop methods that learn more efficiently from limited labeled data and also exhibit better overall relation extraction performance, especially in domains with complex relational structure. In this thesis, I propose to use transfer learning to address this problem, i.e., to reuse knowledge from related tasks to improve models, in particular, their performance and efficiency to learn from limited labeled data. I show how sequential transfer learning, specifically unsupervised language model pre-training, can improve performance and sample efficiency in supervised and distantly supervised relation extraction. In the light of improved modeling abilities, I observe that better understanding neural network-based relation extraction methods is crucial to gain insights that further improve their performance. I therefore present an approach to uncover the linguistic features of the input that neural RE models encode and use for relation prediction. I further complement this with a semi-automated analysis approach focused on model errors, datasets, and annotations. It effectively highlights controversial examples in the data for manual evaluation and allows to specify error hypotheses that can be verified automatically. Together, the researched approaches allow us to build better performing, more sample efficient relation extraction models, and advance our understanding despite their complexity. Further, it facilitates more comprehensive analyses of model errors and datasets in the future.

In the past, the majority of data analysis use cases was addressed by aggregating relational data. Since a few years, a trend is evolving, which is called “Big Data” and which has several implications on the field of data analysis. Compared to previous applications, much larger data sets are analyzed using more elaborate and diverse analysis methods such as information extraction techniques, data mining algorithms, and machine learning methods. At the same time, analysis applications include data sets with less or even no structure at all. This evolution has implications on the requirements on data processing systems. Due to the growing size of data sets and the increasing computational complexity of advanced analysis methods, data must be processed in a massively parallel fashion. The large number and diversity of data analysis techniques as well as the lack of data structure determine the use of user-defined functions and data types. Many traditional database systems are not flexible enough to satisfy these requirements. Hence, there is a need for programming abstractions to define and efficiently execute complex parallel data analysis programs that support custom user-defined operations. The success of the SQL query language has shown the advantages of declarative query specification, such as potential for optimization and ease of use. Today, most relational database management systems feature a query optimizer that compiles declarative queries into physical execution plans. Cost-based optimizers choose from billions of plan candidates the plan with the least estimated cost. However, traditional optimization techniques cannot be readily integrated into systems that aim to support novel data analysis use cases. For example, the use of user-defined functions (UDFs) can significantly limit the optimization potential of data analysis programs. Furthermore, lack of detailed data statistics is common when large amounts of unstructured data is analyzed. This leads to imprecise optimizer cost estimates, which can cause sub-optimal plan choices. In this thesis we address three challenges that arise in the context of specifying and optimizing data analysis programs. First, we propose a parallel programming model with declarative properties to specify data analysis tasks as data flow programs. In this model, data processing operators are composed of a system-provided second-order function and a user-defined first-order function. A cost-based optimizer compiles data flow programs specified in this abstraction into parallel data flows. The optimizer borrows techniques from relational optimizers and ports them to the domain of general-purpose parallel programming models. Second, we propose an approach to enhance the optimization of data flow programs that include UDF operators with unknown semantics. We identify operator properties and conditions to reorder neighboring UDF operators without changing the semantics of the program. We show how to automatically extract these properties from UDF operators by leveraging static code analysis techniques. Our approach is able to emulate relational optimizations such as filter and join reordering and holistic aggregation push-down while not being limited to relational operators. Finally, we analyze the impact of changing execution conditions such as varying predicate selectivities and memory budgets on the performance of relational query plans. We identify plan patterns that cause significantly varying execution performance for changing execution conditions. Plans that include such risky patterns are prone to cause problems in presence of imprecise optimizer estimates. Based on our findings, we introduce an approach to avoid risky plan choices. Moreover, we present a method to assess the risk of a query execution plan using a machine-learned prediction model. Experiments show that the prediction model outperforms risk predictions which are computed from optimizer estimates.

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Cloud Analytics Market Size 2024-2028

The cloud analytics market size is forecast to increase by USD 74.08 billion at a CAGR of 24.4% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The adoption of hybrid and multi-cloud setups is on the rise, as these configurations enhance data connectivity and flexibility. Another trend driving market growth is the increasing use of cloud security applications to safeguard sensitive data.
However, concerns regarding confidential data security and privacy remain a challenge for market growth. Organizations must ensure robust security measures are in place to mitigate risks and maintain trust with their customers. Overall, the market is poised for continued expansion as businesses seek to leverage the benefits of cloud technologies for data processing and data analytics.

What will be the Size of the Cloud Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing volume of data generated by businesses and the demand for advanced analytics solutions. Cloud-based analytics enables organizations to process and analyze large datasets from various data sources, including unstructured data, in real-time. This is crucial for businesses looking to make data-driven decisions and gain valuable insights to optimize their operations and meet customer requirements. Key industries such as sales and marketing, customer service, and finance are adopting cloud analytics to improve key performance indicators and gain a competitive edge. Both Small and Medium-sized Enterprises (SMEs) and large enterprises are embracing cloud analytics, with solutions available on private, public, and multi-cloud platforms.
Big data technology, such as machine learning and artificial intelligence, are integral to cloud analytics, enabling advanced data analytics and business intelligence. Cloud analytics provides businesses with the flexibility to store and process data In the cloud, reducing the need for expensive on-premises data storage and computation. Hybrid environments are also gaining popularity, allowing businesses to leverage the benefits of both private and public clouds. Overall, the market is poised for continued growth as businesses increasingly rely on data-driven insights to inform their decision-making processes.

How is this Cloud Analytics Industry segmented and which is the largest segment?

The cloud analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments.

Solution

  Hosted data warehouse solutions
  Cloud BI tools
  Complex event processing
  Others


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Solution Insights

The hosted data warehouse solutions segment is estimated to witness significant growth during the forecast period.

Hosted data warehouses enable organizations to centralize and analyze large datasets from multiple sources, facilitating advanced analytics solutions and real-time insights. By utilizing cloud-based infrastructure, businesses can reduce operational costs through eliminating licensing expenses, hardware investments, and maintenance fees. Additionally, cloud solutions offer network security measures, such as Software Defined Networking and Network integration, ensuring data protection. Cloud analytics caters to diverse industries, including SMEs and large enterprises, addressing requirements for sales and marketing, customer service, and key performance indicators. Advanced analytics capabilities, including predictive analytics, automated decision making, and fraud prevention, are essential for data-driven decision making and business optimization.

Furthermore, cloud platforms provide access to specialized talent, big data technology, and AI, enhancing customer experiences and digital business opportunities. Data connectivity and data processing in real-time are crucial for network agility and application performance. Hosted data warehouses offer computational power and storage capabilities, ensuring efficient data utilization and enterprise information management. Cloud service providers offer various cloud environments, including private, public, multi-cloud, and hybrid, catering to diverse business needs. Compliance and security concerns are addressed through cybersecurity frameworks and data security measures, ensuring data breaches and thefts are minimized.

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The Hosted data warehouse solutions s