Exploring Neo4J’S Case Insensitive ‘Contains’ Functionality For Efficient Data Retrieval

Why Neo4j Case Insensitive Contains is Important

In the world of database management systems, Neo4j has gained popularity for its ability to handle complex data relationships efficiently. Its powerful query language, Cypher, provides developers with a wide array of functionalities to manipulate and retrieve data. One such functionality is the case insensitive contains feature, which allows users to perform case-insensitive searches within the database. This feature is an essential tool for developers working with Neo4j, as it offers flexibility and ease in querying data without the need to explicitly specify the exact case of the search term.

Understanding the Basics of Neo4j Case Insensitive Contains

Neo4j’s case insensitive contains feature enables you to search for a specific text pattern, regardless of the case of the letters in the pattern. For instance, if you are searching for the string “John Doe” in a database, you can use case insensitive contains to find all instances of “john doe,” “John Doe,” “JOHN DOE,” and so on.

Implementing Neo4j Case Insensitive Contains in Your Queries

Implementing case insensitive contains in your Neo4j queries is simple. You can use the CONTAINS keyword followed by the tilde (~) symbol, and enclose the search term within slashes (/). For instance, to search for the pattern “john doe” in a property called “name,” your query would be:

MATCH (n:Label)
WHERE n.name CONTAINS ~ /john doe/i
RETURN n

In the above query, the “i” parameter after the search term indicates that the search should be case insensitive.

Using Regular Expressions with Neo4j Case Insensitive Contains

Regular expressions are another powerful tool that can be combined with case insensitive contains in Neo4j. With regular expressions, you can define more complex search patterns, allowing for even greater versatility in querying the database. To use regular expressions with case insensitive contains, simply replace the search term within slashes with your desired regular expression pattern. For example, if you want to search for any instance of “john” or “doe” in a property called “name,” your query would be:

MATCH (n:Label)
WHERE n.name CONTAINS ~ /john|doe/i
RETURN n

Considerations and Limitations of Neo4j Case Insensitive Contains

While case insensitive contains in Neo4j offers significant advantages, it is important to consider its limitations. Firstly, the case insensitive search may have an impact on performance, especially when dealing with large datasets. It is essential to optimize the query and ensure that indexes are properly utilized.

Furthermore, it is worth noting that case insensitive contains is limited to ASCII characters. Non-ASCII characters may not behave as expected in a case-insensitive search, so it is recommended to normalize text to ASCII before performing the search.

Best Practices for Using Neo4j Case Insensitive Contains

To make the most out of Neo4j’s case insensitive contains feature, it is important to follow certain best practices:

1. Use indexes: Ensure that the properties being searched with case insensitive contains are properly indexed to optimize query performance.

2. Optimize the query: Refine the query to retrieve only the necessary data. Minimize unnecessary database scans to improve efficiency.

3. Normalize text: If you’re dealing with non-ASCII characters, ensure to normalize the text to ASCII before performing the case-insensitive search.

FAQs

Q: Can case insensitive contains be used with arrays or lists in Neo4j?
A: Yes, case insensitive contains can be used to search for specific patterns within arrays or lists in Neo4j. By using the ANY keyword, you can check if any element in the array or list contains the specified pattern.

Q: Is Cypher case sensitive?
A: By default, Cypher is case-insensitive. However, when using case insensitive contains, you can override this behavior and explicitly specify that the search should be case insensitive.

Q: How can I use a wildcard with Neo4j case insensitive contains?
A: The slashes used in case insensitive contains can be replaced with wildcard characters such as “.*” to match any number of characters. For example, to search for strings starting with “john,” you can use the regular expression pattern `/john.*/i`.

Q: How can I find all nodes with a specific property using Neo4j case insensitive contains?
A: To find all nodes with a specific property using case insensitive contains, you can simply modify the query to exclude any other conditions and only search for the desired property with case insensitive contains.

Q: How can I use the “NOT EQUAL” operator with Neo4j case insensitive contains?
A: To perform a case insensitive search for a string that is not equal to a specific pattern, you can use the NOT keyword in combination with case insensitive contains. For example, to find all nodes where the “name” property does not contain “john,” you can use `WHERE NOT(n.name CONTAINS ~ /john/i)`.

In conclusion, Neo4j’s case insensitive contains feature offers developers the flexibility to perform case-insensitive searches within the database, simplifying the querying process. By understanding its basics, implementing it in queries, and considering its limitations, developers can leverage this feature effectively in their Neo4j applications.

Is Neo4j Case Sensitive?

When working with databases, understanding whether the system is case sensitive or not is crucial. It affects how data is stored, retrieved, and manipulated. In the case of Neo4j, a widely used graph database management system, it is important to know if it operates in a case-sensitive manner. In this article, we will explore whether Neo4j is case sensitive or not, and provide some common FAQs related to this topic.

Neo4j and Case Sensitivity:

Neo4j is a graph database management system that is known for its high-performance and scalability. It is developed in Java language and provides a flexible, schema-agnostic approach to data storage and retrieval. However, when it comes to case sensitivity, Neo4j does behave in a case-sensitive manner.

What does it mean for Neo4j to be case sensitive?

When we say that Neo4j is case sensitive, it means that it treats uppercase and lowercase letters as distinct entities. This applies to identifiers such as node labels, relationship types, property keys, and property values. For example, if we create a node with the label “User,” it will not be considered the same as a node with the label “user.”

How does Neo4j’s case sensitivity impact data retrieval?

Neo4j’s case sensitivity can have implications when querying and retrieving data from the database. When performing a query, one must be careful to match the case correctly to get the desired result. For example, if we have a node with the label “User” and we execute a query with the label “user,” the result will not include the desired node.

Moreover, Neo4j’s case sensitivity applies to property keys and property values as well. If a property key is defined as “name” with a lowercase “n,” querying with an uppercase “N” will not return the desired results. Similarly, if a property value is entered as “John Doe” and we query with “john doe,” the result will not be matched due to the case sensitivity.

How can developers handle case sensitivity in Neo4j?

To handle case sensitivity in Neo4j, developers need to be consistent in their data entry and queries. Here are a few best practices:

1. Use consistent naming conventions: Adopting a naming convention and sticking to it throughout the database schema can help ensure consistency in the case usage. For example, always using lowercase letters for node labels, relationship types, property keys, and property values can minimize confusion.

2. Be mindful of queries: When writing Cypher queries in Neo4j, make sure to match the case correctly. Double-check the identifiers and values to ensure they are spelled correctly and use the correct case.

3. Leverage case-insensitive comparisons: Although Neo4j is case sensitive by default, it does provide case-insensitive comparison functions that can be utilized to overcome case sensitivity issues. Functions like `toLower()` can be used to convert strings to lowercase, enabling easier matching in queries.

Frequently Asked Questions:

Q: Can we change the default case sensitivity in Neo4j?
A: No, the case sensitivity behavior in Neo4j is fixed and cannot be altered. It is rooted in the way the underlying database engine is designed.

Q: What happens if I try to create duplicate labels or relationship types with different cases in Neo4j?
A: Neo4j treats labels and relationship types with different cases as distinct entities. Hence, you will be able to create duplicates using different case variations.

Q: Are indexes affected by case sensitivity in Neo4j?
A: Yes, when creating indexes in Neo4j, you need to ensure that the case used for the indexed property matches exactly when querying. Otherwise, the index may not yield the desired results.

Q: Can I ignore case sensitivity in Neo4j completely?
A: While you cannot disable case sensitivity, as mentioned earlier, you can utilize case-insensitive comparison functions in your Cypher queries to overcome this limitation.

Q: Does Neo4j’s case sensitivity impact performance?
A: Neo4j’s case sensitivity does not have a significant impact on performance. The database engine is designed to efficiently handle case-sensitive operations.

Conclusion:

In conclusion, Neo4j is a case-sensitive graph database management system. It treats uppercase and lowercase letters as distinct entities when it comes to identifiers, node labels, relationship types, property keys, and property values. Developers need to be mindful of this behavior when querying and retrieving data. By following certain best practices and leveraging case-insensitive comparison functions when needed, developers can effectively work with Neo4j while managing case sensitivity challenges.

Can relationships have properties in Neo4j?

Neo4j, the popular graph database, provides a flexible and powerful platform for managing and analyzing relationships between data. It allows for the creation of complex networks and enables the storage of additional information within relationships. These additional properties attached to relationships make them more expressive and provide a deeper understanding of the connections between data points.

In Neo4j, relationships are not simply connections; they can be defined as first-class citizens with their own properties. These properties can be assigned to relationships and can hold any type of data, such as strings, numbers, booleans, or even arrays.

Adding properties to relationships allows for a more detailed representation of real-world scenarios. For example, suppose we have a social network graph where people are connected by relationships labelled “FRIENDS”. By adding properties to these relationships, we can capture additional information, such as the date the friendship was established, the level of trust between friends, or the number of interactions they have had.

The ability to attach properties to relationships also enables the storage of metadata that is specific to the relationship itself. This metadata can be used for various purposes, such as filtering, sorting, or querying. It allows for a more nuanced analysis of the data and opens up possibilities for complex data modeling.

To add properties to relationships in Neo4j, a key-value pair system is used. Each property is defined by a key, which acts as an identifier, and a value, which holds the actual data. These properties can be added when creating a relationship or updated later as needed.

Neo4j provides a query language called Cypher that allows users to interact with the database and perform various operations, including creating or updating relationships. Adding properties to relationships is as simple as including the key-value pairs in the Cypher CREATE or SET clause. For example, to create a relationship between two nodes and assign properties to it, the Cypher query might look like this:

“`
MATCH (a:Person {name: ‘Alice’}), (b:Person {name: ‘Bob’})
CREATE (a)-[r:FRIENDS {since: ‘2020-01-01’, trust: 0.8}]->(b)
“`

In this example, a new relationship labeled FRIENDS is created between nodes representing Alice and Bob, with properties “since” and “trust” assigned to it.

Once the properties are attached to relationships, they can be accessed and queried just like any other data in the graph. This flexibility allows for complex and customized analyses. Properties can be used in WHERE clauses to filter relationships based on specific criteria, or they can be aggregated and used for calculations.

FAQs:

Q: Can relationships in Neo4j have multiple properties?
A: Yes, relationships in Neo4j can have multiple properties. Each property is defined by a key-value pair.

Q: Can properties be added or updated after the relationship is created?
A: Yes, properties can be added or updated at any time using the Cypher query language.

Q: What types of data can properties hold?
A: Properties in Neo4j can hold any type of data, such as strings, numbers, booleans, or arrays.

Q: How can properties be accessed and queried?
A: Properties can be accessed and queried using the Cypher query language, just like any other data in the graph.

Q: Do properties affect the performance of queries?
A: The impact of properties on query performance depends on the size and complexity of the graph. It’s important to consider indexing and query optimization techniques to ensure efficient retrieval of data.

Q: Can relationships have properties in other graph databases?
A: The support for relationship properties varies across graph databases. However, Neo4j provides extensive support for properties on relationships, making it a popular choice for relationship-centric data modeling and analysis.

In conclusion, relationships in Neo4j can have properties, allowing for a more comprehensive representation and analysis of data connections. These properties enable the storage of additional information specific to each relationship, leading to more nuanced queries and insights. The ability to attach properties to relationships is a powerful feature of Neo4j that sets it apart as a leading graph database.

Title: Neo4j: Unleashing the Power of Graph Databases

Introduction:
In recent years, graph databases have emerged as a powerful solution for managing complex data structures. Neo4j, a leading graph database, offers groundbreaking capabilities that allow users to unlock hidden insights from their interconnected data. In this article, we will delve deep into the world of Neo4j, exploring its features, use cases, advantages, and frequently asked questions.

Understanding Neo4j:
Neo4j is an open-source, highly scalable, and ACID-compliant graph database management system. It is designed to efficiently store, manage, and query highly interconnected data. Unlike traditional relational databases, Neo4j treats data as nodes and relationships, making it ideal for use cases where complex relationships need to be modeled and analyzed.

Key Features of Neo4j:
1. Graph-Based Model: Neo4j’s power lies in its ability to represent relationships as first-class citizens. This allows for the creation of flexible and rich data models, making it easy to navigate and derive meaningful insights from interconnected data.

2. Cypher Query Language: Neo4j utilizes the Cypher query language, specifically designed for graph-based querying. Cypher’s intuitive syntax makes it easy to express complex queries that traverse paths, patterns, and relationships within the graph.

3. Scalability: Neo4j’s architecture enables efficient scaling across multiple machines, accommodating growing datasets with ease. It provides horizontal scale-out options and can handle billions of nodes and relationships.

4. Performance: Neo4j’s graph-based nature promotes highly performant querying, especially for complex relationship-based queries. Its native index-free adjacency ensures that traversing relationships is blazingly fast.

5. Data Integrity: Neo4j maintains strong ACID (Atomicity, Consistency, Isolation, Durability) guarantees, ensuring data integrity and consistency even in the face of concurrent updates.

Use Cases for Neo4j:
1. Social Network Analysis: Neo4j empowers social media platforms, allowing them to analyze relationships between users, interests, and activities. It enables the identification of trends, influencers, and recommendation systems that enhance user experiences.

2. Fraud Detection: By modeling and analyzing complex networks of transactions, Neo4j enables the fast and accurate detection of fraudulent activities. Relationships between individuals, accounts, and transactions can be easily explored to identify suspicious patterns.

3. Recommendation Systems: Neo4j can power highly personalized and relevant recommendation engines. By analyzing user behavior, item attributes, and relationships, it enables the efficient extraction of recommendations based on similar users’ interests.

4. Knowledge Graphs: Organizations can utilize Neo4j to create knowledge graphs, consolidating diverse data sources into a single unified view. By capturing relationships between concepts, knowledge graphs enable advanced search and reasoning capabilities.

Advantages of Neo4j:
1. Flexibility: The graph-based data model of Neo4j allows for highly flexible and agile development. It adapts to changing requirements and complex data structures effortlessly.

2. Performance: Neo4j’s graph database model ensures that queries involving relationships are performed at high speeds. It eliminates the need for complex JOIN operations, resulting in efficient data retrieval.

3. Expressive Querying: The Cypher query language makes it easy to express intricate queries involving patterns and relationships within the graph. It promotes ease of use and accelerates development and analysis.

4. Scalability: Neo4j’s distributed architecture allows for seamless scaling as data volume increases. It supports horizontal scale-out to handle billions of nodes and relationships efficiently.

Frequently Asked Questions:

Q1. Is Neo4j only suitable for large-scale enterprise applications?
Neo4j is designed to accommodate applications of all sizes. It can be used by small startups, medium-sized companies, as well as large enterprises. Its scalability allows it to grow with the application’s needs.

Q2. Does Neo4j support multiple data models?
Neo4j primarily focuses on the property graph data model. However, it also supports RDF triple stores, enabling users to benefit from both models simultaneously.

Q3. What programming languages can be used with Neo4j?
Neo4j provides official drivers for popular programming languages including Java, Python, C#, JavaScript, and others. Additionally, a vibrant community ecosystem enables support for various frameworks and languages.

Q4. Is Neo4j suitable for real-time applications?
Absolutely. Neo4j’s optimized query engine and high-performance capabilities ensure quick response times, making it suitable for real-time applications.

Q5. How does Neo4j compare to relational databases?
Neo4j’s graph model excels in situations where relationships between entities are crucial. Relational databases are better suited for structured data that does not rely heavily on relationships. Neo4j offers faster querying of complex relationship-based patterns and supports more natural data modeling.

Conclusion:
Neo4j, with its cutting-edge graph database architecture, has revolutionized the way complex, interconnected data is managed and analyzed. Its powerful features, scalability, and flexibility make it an excellent choice for applications requiring in-depth relationship analysis. By harnessing the potential of Neo4j, organizations can derive meaningful insights and drive innovation like never before.

Neo4j Array Contains: Exploring the Power of Dynamic Arrays in Graph Databases

Introduction:

In the realm of graph databases, Neo4j has emerged as a powerful tool. With its flexible data model and ability to efficiently handle complex relationships, Neo4j allows developers to build highly efficient applications. One feature that sets Neo4j apart from its competitors is the ability to use arrays and perform array contains operations. In this article, we will dive deep into the concept of array contains in Neo4j, exploring its benefits, use cases, and potential pitfalls.

Understanding Array Contains in Neo4j:

Array contains is an operation that allows us to determine whether an array contains a specific element. In Neo4j, arrays can be stored as a property of a node or a relationship. This capability enables developers to efficiently query arrays and find relevant data in a graph database.

The syntax for array contains in Cypher, the query language for Neo4j, is as follows:

MATCH (n:Node)
WHERE element IN n.arrayProperty
RETURN n

Here, “Node” represents the node label, “arrayProperty” refers to the property containing the array, and “element” represents the value we are searching for within the array.

Benefits of Array Contains in Neo4j:

1. Efficient Searching: Array contains operations in Neo4j are optimized and perform efficiently even on large arrays. This allows for quick retrieval of relevant data, improving overall application performance.

2. Flexibility: Arrays in Neo4j can be of any length and can contain elements of different types. This flexibility enables developers to model data in a way that best suits their application needs.

3. Seamless Integration: With array contains, developers can easily integrate Neo4j with other tools and technologies. This integration empowers them to create complex data structures and perform advanced analytics on their data.

Use Cases for Array Contains in Neo4j:

1. Social Networks: Array contains can be leveraged to model friend lists, where the array property holds the unique identifiers of a user’s friends. This allows for efficient retrieval of a user’s connections and enables the implementation of features like friend suggestions.

2. E-commerce: Array contains can be used to store product tags or categories associated with an item. This enables efficient filtering and searching based on specific tags, enhancing the user experience and improving search performance.

3. IoT (Internet of Things): Neo4j’s array contains can be utilized to store and query measurements from multiple sensors, where each element in the array represents a specific sensor reading. This allows for efficient analysis and monitoring of sensor data.

4. Recommendations: Array contains can aid in building recommendation systems, where arrays hold the unique identifiers of items a user has interacted with or purchased. By finding common elements in different arrays, personalized recommendations can be generated.

FAQs:

Q1. Can I store arrays of any size in Neo4j?
Yes, Neo4j allows for the storage of arrays of any size. This flexibility enables developers to handle arrays of varying lengths based on their specific requirements.

Q2. Can an array property hold elements of different data types?
Yes, Neo4j supports arrays that can hold elements of different types. This flexibility enables developers to store heterogeneous data within the same array property.

Q3. Can I perform array contains operations on nested arrays?
Yes, Neo4j’s array contains operations can be performed on nested arrays. This allows for powerful querying capabilities, enabling developers to find specific elements even within complex data structures.

Q4. Are there any limitations to using array contains in Neo4j?
While array contains is a powerful feature in Neo4j, it’s important to note that extensive use of array operations on large arrays can impact query performance. It’s advisable to evaluate and optimize queries with large arrays to ensure efficient execution.

Q5. Can I use array contains in conjunction with other Cypher query operations?
Yes, array contains can be combined with other Cypher query operations, such as filtering, sorting, and aggregating. This allows for complex querying capabilities and enables developers to extract valuable insights from their data.

Conclusion:

Neo4j’s array contains feature provides developers with a powerful tool for handling data stored as arrays in a graph database. With its efficiency, flexibility, and seamless integration capabilities, array contains empowers developers to build highly efficient and personalized applications. By understanding the benefits and use cases of array contains in Neo4j, developers can leverage this feature to unlock the true potential of their graph databases.

Neo4j: The Revolutionary Graph Database

In the rapidly evolving field of data management, graph databases have emerged as a game-changing solution for handling complex and interconnected datasets. Among the leading players in this space, Neo4j stands out as a powerful and flexible graph database management system that has empowered numerous organizations to leverage the true potential of their data. In this article, we will delve into the intricacies of Neo4j, exploring its key features, advantages, and use cases. So, fasten your seatbelts as we embark on a journey through the world of Neo4j!

What is Neo4j?

Neo4j is an open-source graph database management system built around the concept of nodes and relationships. Unlike traditional relational databases that rely on tables and rows, Neo4j focuses on representing data as a network of interconnected nodes connected by relationships. This unique approach allows for highly efficient storage, processing, and querying of complex relationships, making it an ideal choice for applications that rely heavily on data relationships and their analysis.

Key Features:

1. Native Graph Technology: Unlike other databases that emulate graph features, Neo4j is specifically designed to natively store, process, and query graph data. This inherent support for graphs makes Neo4j a natural choice for use cases requiring deep relationships and complex querying.

2. ACID Compliance: Neo4j ensures data integrity and consistency through full ACID compliance. This means that transactions in Neo4j are reliable, atomic, consistent, isolated, and durable, making it suitable for applications demanding strong data integrity guarantees.

3. Advanced Query Language: Neo4j employs its own query language called Cypher. Cypher is a powerful, SQL-like declarative language specifically tailored for querying graph data. It allows users to easily express complex graph patterns and retrieve information efficiently. Its intuitive syntax has gained popularity among developers and data scientists alike.

4. High Performance: Neo4j’s graph-based architecture enables lightning-fast querying and traversing of relationships. This makes it an ideal choice for applications that demand real-time analysis, such as social networks, recommendation engines, fraud detection, and network management, among others.

5. Scalability and Flexibility: Neo4j’s distributed architecture allows for easy horizontal scalability, making it suitable for handling large and ever-growing datasets. Its flexible data model enables easy addition and modification of nodes and relationships without affecting the overall system performance.

Advantages of Neo4j:

1. Powerful Relationship Management: Neo4j’s native graph technology allows for efficient and seamless management of highly interconnected and complex relationships. This provides a significant advantage over traditional databases, where such relationships are often challenging to represent and query efficiently.

2. Intuitive Data Modeling: Neo4j’s graph-based data model closely resembles the way humans naturally perceive and understand data relationships. Its intuitive nature simplifies data modeling and enables developers to create highly expressive and accurate representations of real-world scenarios.

3. Deep Insights and Real-time Analysis: By leveraging the power of graph-based querying, Neo4j enables organizations to uncover hidden insights and patterns within their data. This helps make informed decisions, identify trends, and gain a competitive edge in real-time.

4. Faster Time-to-Market: Neo4j’s elegant data model and powerful query language significantly reduce development time and effort. Its ability to directly model complex relationships without complex joins or foreign keys translates into faster application development and quicker time-to-market.

5. Community Support and Ecosystem: Neo4j boasts a vibrant and active community that actively contributes libraries, extensions, and integrations, enhancing its functionality and usability. The extensive ecosystem surrounding Neo4j ensures continuous support and growth, making it a reliable choice for long-term projects.

Use Cases:

1. Social Networks: Multiple social platforms have leveraged Neo4j’s capabilities to build robust and scalable social networks. Neo4j allows efficient management of millions of user profiles, relationships, recommendations, and personalized content delivery.

2. Recommendation Engines: Neo4j’s graph-based querying enables the development of highly accurate and efficient recommendation engines. By modeling user preferences, items, and relevant relationships, Neo4j enables personalized recommendations at scale.

3. Fraud Detection: Neo4j’s ability to detect patterns and relationships in real-time makes it a powerful tool for fraud detection. By analyzing interconnected data from multiple sources, Neo4j can identify suspicious patterns and take appropriate actions promptly.

4. Master Data Management: Organizations dealing with complex, interconnected master data can benefit from Neo4j’s graph capabilities. Neo4j simplifies data modeling and provides efficient querying and management of complex relationships, ensuring data consistency and accuracy across the organization.

5. Internet of Things (IoT): The Internet of Things generates massive amounts of interconnected data that can be efficiently stored, processed, and analyzed using Neo4j. Its ability to represent and query complex relationships makes it an ideal choice for managing IoT data, enabling smarter decision-making and predictive analytics.

FAQs:

Q: Is Neo4j suitable for small-scale applications?
A: Yes, Neo4j’s flexibility and scalability make it suitable for applications of all sizes, from small-scale projects to enterprise-level deployments.

Q: Can Neo4j handle high-concurrency environments?
A: Yes, Neo4j’s architecture is designed to handle high-concurrency scenarios, ensuring optimal performance even in demanding environments.

Q: Is Neo4j compatible with other database systems?
A: Neo4j can be integrated with various systems through its extensive ecosystem and APIs, allowing seamless data exchange with other databases.

Q: Is Cypher easy to learn?
A: Yes, Cypher’s intuitive syntax makes it relatively easy to learn, especially for those familiar with SQL or other query languages.

Q: Is Neo4j suitable for time-series data?
A: While Neo4j excels in managing complex relationships and graph-based data, it may have limitations when handling massive amounts of purely time-series data. In such cases, it is advisable to explore specialized time-series databases.

In conclusion, Neo4j has revolutionized the way we handle and analyze complex data by leveraging the power of graph databases. Its native graph technology, advanced query language, and scalability make it a formidable choice for applications requiring deep relationships and real-time analysis. With a vibrant community and an extensive ecosystem, Neo4j continues to evolve and innovate, further solidifying its position as a leading graph database management system. Whether you’re building social networks, recommendation engines, or tackling fraud detection, Neo4j provides the foundation for unlocking the true potential of your data.

Found 18 images related to neo4j case insensitive contains theme