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A graph database GDB is a database that uses graph structures for semantic queries with nodes , edges , and properties to represent and store data. The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation.

Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database.

Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data. Graph databases are commonly referred to as a NoSQL. Graph databases are similar to s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction [3] and lack easy traversal over a chain of edges. The underlying storage mechanism of graph databases can vary. Relationships are a first-class citizen in a graph database and can be labelled, directed, and given properties.

Some depend on a relational engine and “store” the graph data in a table although a table is a logical element, therefore this approach imposes another level of abstraction between the graph database, the graph database management system and the physical devices where the data is actually stored.

Others use a key—value store or document-oriented database for storage, making them inherently NoSQL structures. As of [update] , no universal graph query language has been adopted in the same way as SQL was for relational databases, and there are a wide variety of systems, most often tightly tied to one product. In addition to having query language interfaces, some graph databases are accessed through application programming interfaces APIs. Graph databases differ from graph compute engines.

Graph databases are technologies that are translations of the relational online transaction processing OLTP databases. On the other hand, graph compute engines are used in online analytical processing OLAP for bulk analysis. Graph databases attracted considerable attention in the s, due to the successes of major technology corporations in using proprietary graph databases, [5] along with the introduction of open-source graph databases.

One study concluded that an RDBMS was “comparable” in performance to existing graph analysis engines at executing graph queries. In the mids, navigational databases such as IBM ‘s IMS supported tree -like structures in its hierarchical model , but the strict tree structure could be circumvented with virtual records. Graph structures could be represented in network model databases from the late s. Labeled graphs could be represented in graph databases from the mids, such as the Logical Data Model.

In , the Object Data Management Group published a standard language for defining object and relationship graph structures in their ODMG’93 publication. Several improvements to graph databases appeared in the early s, accelerating in the late s with endeavors to index web pages.

In the s, commercial ACID graph databases that could be scaled horizontally became available. During this time, graph databases of various types have become especially popular with social network analysis with the advent of social media companies.

Graph databases portray the data as it is viewed conceptually. This is accomplished by transferring the data into nodes and its relationships into edges. A graph database is a database that is based on graph theory.

It consists of a set of objects, which can be a node or an edge. A labeled-property graph model is represented by a set of nodes, relationships, properties, and labels.

Both nodes of data and their relationships are named and can store properties represented by key—value pairs. Nodes can be labelled to be grouped. The edges representing the relationships have two qualities: they always have a start node and an end node, and are directed; [12] making the graph a directed graph.

Relationships can also have properties. This is useful in providing additional metadata and semantics to relationships of the nodes. In an RDF graph model, the addition of information is each represented with a separate node. For example, imagine a scenario where a user has to add a name property for a person represented as a distinct node in the graph.

In a labeled-property graph model, this would be done with an addition of a name property into the node of the person. However, in an RDF, the user has to add a separate node called hasName connecting it to the original person node.

Specifically, an RDF graph model is composed of nodes and arcs. An RDF graph notation or a statement is represented by: a node for the subject, a node for the object, and an arc for the predicate. An arc may also be identified by a URI.

A literal for a node may be of two types: plain untyped and typed. A plain literal has a lexical form and optionally a language tag. A typed literal is made up of a string with a URI that identifies a particular datatype. A blank node may be used to accurately illustrate the state of the data when the data does not have a URI. Graph databases are a powerful tool for graph-like queries. For example, computing the shortest path between two nodes in the graph. Other graph-like queries can be performed over a graph database in a natural way for example graph’s diameter computations or community detection.

Graphs are flexible, meaning it allows the user to insert new data into the existing graph without loss of application functionality. There is no need for the designer of the database to plan out extensive details of the database’s future use cases. Data lookup performance is dependent on the access speed from one particular node to another.

Because index -free adjacency enforces the nodes to have direct physical RAM addresses and physically point to other adjacent nodes, it results in a fast retrieval. A native graph system with index-free adjacency does not have to move through any other type of data structures to find links between the nodes.

Directly related nodes in a graph are stored in the cache once one of the nodes are retrieved, making the data lookup even faster than the first time a user fetches a node.

However, such advantage comes at a cost. Index-free adjacency sacrifices the efficiency of queries that do not use graph traversals. Native graph databases use index-free adjacency to process CRUD operations on the stored data. There are multiple types of graphs that can be categorized. Gartner suggests the five broad categories of graphs: [16]. Since Edgar F. Codd ‘s paper on the relational model , [17] relational databases have been the de facto industry standard for large-scale data storage systems.

Relational models require a strict schema and data normalization which separates data into many tables and removes any duplicate data within the database.

Data is normalized in order to preserve data consistency and support ACID transactions. However this imposes limitations on how relationships can be queried. One of the relational model’s design motivations was to achieve a fast row-by-row access. Although relationships can be analyzed with the relational model, complex queries performing many join operations on many different attributes over several tables are required. In working with relational models, foreign key constraints should also be considered when retrieving relationships, causing additional overhead.

Compared with relational databases , graph databases are often faster for associative data sets [ citation needed ] and map more directly to the structure of object-oriented applications. They can scale more naturally [ citation needed ] to large datasets as they do not typically need join operations, which can often be expensive.

As they depend less on a rigid schema, they are marketed as more suitable to manage ad hoc and changing data with evolving schemas. Conversely, relational database management systems are typically faster at performing the same operation on large numbers of data elements, permitting the manipulation of the data in its natural structure.

Despite the graph databases’ advantages and recent popularity over [ citation needed ] relational databases, it is recommended the graph model itself should not be the sole reason to replace an existing relational database. A graph database may become relevant if there is an evidence for performance improvement by orders of magnitude and lower latency. The relational model gathers data together using information in the data.

For example, one might look for all the “users” whose phone number contains the area code “”. This would be done by searching selected datastores, or tables , looking in the selected phone number fields for the string “”. This can be a time-consuming process in large tables, so relational databases offer indexes , which allow data to be stored in a smaller sub-table, containing only the selected data and a unique key or primary key of the record.

If the phone numbers are indexed, the same search would occur in the smaller index table, gathering the keys of matching records, and then looking in the main data table for the records with those keys.

Usually, a table is stored in a way that allows a lookup via a key to be very fast. Relational databases do not inherently contain the idea of fixed relationships between records. Instead, related data is linked to each other by storing one record’s unique key in another record’s data. For example, a table containing email addresses for users might hold a data item called userpk , which contains the primary key of the user record it is associated with.

In order to link users and their email addresses, the system first looks up the selected user records primary keys, looks for those keys in the userpk column in the email table or, more likely, an index of them , extracts the email data, and then links the user and email records to make composite records containing all the selected data.

This operation, termed a join , can be computationally expensive. Depending on the complexity of the query, the number of joins, and indexing various keys, the system may have to search through multiple tables and indexes and then sort it all to match it together. In contrast, graph databases directly store the relationships between records. Instead of an email address being found by looking up its user’s key in the userpk column, the user record contains a pointer that directly refers to the email address record.

That is, having selected a user, the pointer can be followed directly to the email records, there is no need to search the email table to find the matching records. This can eliminate the costly join operations. For example, if one searches for all of the email addresses for users in area code “”, the engine would first perform a conventional search to find the users in “”, but then retrieve the email addresses by following the links found in those records.

A relational database would first find all the users in “”, extract a list of the primary keys, perform another search for any records in the email table with those primary keys, and link the matching records together. For these types of common operations, graph databases would theoretically be faster. The true value of the graph approach becomes evident when one performs searches that are more than one level deep.

For example, consider a search for users who have “subscribers” a table linking users to other users in the “” area code. In this case a relational database has to first search for all the users with an area code in “”, then search the subscribers table for any of those users, and then finally search the users table to retrieve the matching users. In contrast, a graph database would search for all the users in “”, then follow the backlinks through the subscriber relationship to find the subscriber users.

This avoids several searches, look-ups, and the memory usage involved in holding all of the temporary data from multiple records needed to construct the output.

 
 

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The AI Hardware Summit is the premier commercial event focused on systems-first machine learning. We are creating a feedback loop between those designing AI systems and those using them, to accelerate the development and adoption of AI technologies across industries. This year, for the first time, we will be co-located with Edge AI Summit.

PLUS, we have exclusive group booking discounts available! View the full agenda here. Prior to Meta, Dr. Prior to Intel, she spent eight years as President of Source Photonics, where she also served on the board of directors. She earned a B. He is a widely recognized expert in distributed systems, operating system internals, and cybersecurity.

Russinovich joined Microsoft in when Microsoft acquired Winternals Software, the company he cofounded in , as well as Sysinternals, where he authors and publishes dozens of popular Windows administration and diagnostic utilities.

Sassine Ghazi leads and drives strategy for all business units, sales and customer success, strategic alliances, marketing and communications at Synopsys.

He joined the company in as an applications engineer. He then held a series of sales positions with increasing responsibility, culminating in leadership of worldwide strategic accounts. He was then appointed general manager for all digital and custom products, the largest business group in Synopsys.

Under his leadership, several innovative solutions were launched in areas such as multi-die systems, AI-assisted design and silicon lifecycle management. He assumed the role of chief operating officer in August, and was appointed to the role of president in November Prior to Synopsys he was a design engineer at Intel.

E from the Georgia Institute of Technology and an M. She drove AI research to production innovations across hardware acceleration, enabling model exploration and model scale, building production ecosystem and platforms for on-device and privacy-preserving AI. She received a Ph. Prior to Facebook, she worked on a broad range of distributed and data processing domains, from high-performance columnar databases, OLTP systems, streaming processing systems, data warehouse systems, and logging and metrics platforms.

Joseph Sawicki is a leading expert in IC nanometer design and manufacturing challenges. Formerly responsible for Mentor’s industry-leading design-to-silicon products, including the Calibre physical verification and DFM platform and Mentor’s Tessent design-for-test product line, Sawicki now oversees all business units in the Siemens EDA IC segment. Sawicki joined Mentor Graphics in and has held previous positions in applications engineering, sales, marketing, and management.

Judy has over 25 years of experience in developing data centers systems and silicon, high speed signaling technologies and optics, circuit design, and physical architectures for compute, storage, graphics, and networking. Tan also serves on the Boards of Softbank and Schneider Electric. Lip-Bu holds a B. Xiong holds a Ph. Agus Sudjianto is an executive vice president, head of Model Risk and a member of the Management Committee at Wells Fargo, where he is responsible for enterprise model risk management.

Prior to his current position, Agus was the modeling and analytics director and chief model risk officer at Lloyds Banking Group in the United Kingdom. Prior to his career in banking, he was a product design manager in the Powertrain Division of Ford Motor Company. Agus holds several U. He has published numerous technical papers and is a co-author of Design and Modeling for Computer Experiments.

His technical expertise and interests include quantitative risk, particularly credit risk modeling, machine learning and computational statistics. He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.

Divya Jain is an industry recognized product and technology leader in machine learning and AI. Before this she was a Research Director at Tyco Innovation Garage and led various deep learning initiatives in video surveillance space. She also co-founded a startup, dLoop Inc. At Box, Divya led the team that built the first machine learning capabilities into the Box platform. She is very passionate about open sharing of knowledge and information and always working towards abridging technology gap for product innovation.

As Chief Business Officer of DeepMind, he oversees a wide-range of teams including Applied , which applies research breakthroughs to Google products and infrastructure used by billions of people. He also helps drive the growth of DeepMind, building and leading critical functions including finance and strategy and leading external and commercial partnerships. Originally an electronics and software engineer, he has held senior positions at both start-ups and global companies such as Thomson Reuters, helping them solve their own complex, mission-critical, real-world challenges.

His diverse professional background also includes building point of care expert systems for physicians to improve quality of care, co-founding an online personal finance marketplace, and building an online real estate brokerage platform. Dan is a distinguished engineer in AI working on innovative database architectures including document and graph databases.

He has a strong background in semantics, ontologies, NLP and search. He is a hands-on architect and like to build his own pilot applications using new technologies. Conference now called the Database Now! He has lead algorithm development and generated valuable insights to improve medical products ranging from implantable, wearable medical and imaging devices to bioinformatics and pharmaceutical products for a variety of multinational medical companies.

He has lead projects, data science teams and developed algorithms for closed loop active medical implants e. Pacemakers, cochlear and retinal implants as well as advanced computational biology to study the time evolution of cellular networks associated with cancer , depression and other illnesses. Recently, his team has been working on cutting edge natural language processing and developed cutting edge models to address the healthcare challenges dealing with textual data.

He is experienced in developing scalable Machine Learning solutions to solve big data problems that involve text and multimodal data. Previously, Cedric received his Ph. Olukotun is a renowned pioneer in multi-core processor design and the leader of the Stanford Hydra chip multiprocessor CMP research project.

Prior to SambaNova Systems, Olukotun founded Afara Websystems to develop high-throughput, low-power multi-core processors for server systems. Goode Memorial Award and was also elected to the National Academy of Engineering—one of the highest professional distinctions accorded to an engineer. He has been designing original processors for emergent workloads for over 30 years, focussing on intelligence since Before Graphcore, Simon co-founded two other successful processor companies — Element14, acquired by Broadcom in , and Icera, acquired by Nvidia in She has also worked as an early stage investor at Earlybird Venture Capital, a premier European venture capital fund based in Germany.

She is also a Kauffman Fellow. Cade Metz is a reporter with The New York Times, covering artificial intelligence, driverless cars, robotics, virtual reality, and other emerging areas. Genius Makers is his first book. Previously, he was a senior staff writer with Wired magazine and the U. Her research focuses on designing systems for at-scale execution of machine learning, such as personalized recommender systems and for mobile deployment.

More generally, her research interests are in computer architecture with particular focus on energy- and memory-efficient systems. She received her M. I was drawn to Rambus to focus on cutting edge computing technologies. At Rambus, we are solving challenges that are completely new to the industry and occur as a response to deployments that are highly sophisticated and advanced.

As an inventor, I find myself approaching a challenge like a room filled with , pieces of a puzzle where it is my job to figure out how they all go together — without knowing what it is supposed to look like in the end.

For me, the job of finishing the puzzle is as enjoyable as the actual process of coming up with a new, innovative solution. We did a lot of novel things that required inventiveness — we pushed the envelope and created state of the art performance without making actual changes to the infrastructure. After an inspiring work day at Rambus, I enjoy spending time with my family, being outdoors, swimming, and reading.

He is an AI and machine learning technologist, entrepreneur and engineer with real-world experience in taking cutting edge research from ideation stage to scalable products. Karl works with investment and technology customers to help them understand the emerging Deep Learning opportunity in data centers, from competitive landscape to ecosystem to strategy.

Karl has worked directly with datacenter end users, OEMs, ODMs and the industry ecosystem, enabling him to help his clients define the appropriate business, product, and go-to-market strategies. He is also recognized expert on the subject of low-power servers and the emergence of ARM in the datacenter and has been a featured speaker at scores of investment and industry conferences on this topic.

Rashmi Gopinath is a General Partner at B Capital Group where she focuses on investments in cloud infrastructure, cybersecurity, devops, and artificial intelligence and machine learning. She brings over two decades of experience investing and operating in enterprise technologies.

Gopinath was previously a Managing Director at M12, Microsoft’s venture fund, where she led investments globally in the enterprise space. At M12, Ms. Prior to M12, Ms. Gopinath held operating roles at high-growth startups such as BlueData and Couchbase where she led global business development and product marketing roles.

She began her career in engineering and product roles at Oracle and GE Healthcare. Gopinath earned an M. Weifeng Zhang is the Chief Scientist of Heterogeneous Computing at Alibaba Cloud Infrastructure, responsible for performance optimization of large scale distributed applications at the data centers.

Weifeng also leads the effort to build the acceleration platform for various ML workloads via heterogeneous resource pooling based on the compiler technology. Weifeng received his B. Marshall has extensive experience leading global organizations to bring breakthrough products to market, establish new market presences, and grow new and existing lines of business. He was responsible for the portfolio and strategy for Oracle Systems products and solutions. He led teams that delivered comprehensive end-to-end hardware and software solutions and prod.

He led teams that delivered comprehensive end-to-end hardware and software solutions and product management operations. During his 11 years there, Marshall held various positions in development, information technology, and marketing.

This means members are served financial products, tools and recommendations at the right time, and that best adhere to their financial goals and ambitions.

Today, her business unit partners with nearly every team at Credit Karma and drives a significant portion of revenue for the business. Prior to joining Credit Karma, Supriya worked in product at Facebook, where she led product strategy and development for a number of ads products.

Supriya holds a B. Stelios heads Synopsys’ AI Solutions team in the Office of the President, where he researches and applies innovative machine-learning technology to address systemic complexity in the design and manufacturing of integrated computational systems. In , Stelios launched DSO.

 

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