Security information management (SIM) is an information security industry term for the collection of data such as log files into a central repository for trend analysis. == Overview == SIM products generally are software agents running on the computer systems that are monitored. The recorded log information is then sent to a centralized server that acts as a "security console". The console typically displays reports, charts, and graphs of that information, often in real time. Some software agents can incorporate local filters to reduce and manipulate the data that they send to the server, although typically from a forensic point of view you would collect all audit and accounting logs to ensure you can recreate a security incident. The security console is monitored by an administrator who reviews the consolidated information and takes action in response to any alerts issued. The data that is sent to the server to be correlated and analyzed are normalized by the software agents into a common form, usually XML. Those data are then aggregated in order to reduce their overall size. == Terminology == The terminology can easily be mistaken as a reference to the whole aspect of protecting one's infrastructure from any computer security breach. Due to historic reasons of terminology evolution; SIM refers to just the part of information security which consists of discovery of 'bad behavior' or policy violations by using data collection techniques. The term commonly used to represent an entire security infrastructure that protects an environment is commonly called information security management (InfoSec). Security information management is also referred to as log management and is different from SEM (security event management), but makes up a portion of a SIEM (security information and event management) solution. == Regulatory compliance == Security information management systems support compliance with regulatory frameworks that require centralized collection and analysis of security data. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to implement audit controls that record and examine activity in information systems containing electronic protected health information (45 CFR 164.312(b))."45 CFR § 164.312 - Technical safeguards". Legal Information Institute. Retrieved April 1, 2026. SIM platforms aggregate these audit records to support the required regular review of information system activity records (45 CFR 164.308(a)(1)(ii)(D)). The December 2024 HIPAA Security Rule NPRM proposed requiring regulated entities to deploy automated systems capable of monitoring and recording access to ePHI, including the ability to detect unauthorized access attempts in near real-time."HIPAA Security Rule To Strengthen the Cybersecurity of Electronic Protected Health Information". Federal Register. January 6, 2025. Retrieved April 1, 2026. The Payment Card Industry Data Security Standard (PCI DSS) similarly requires centralized log management and daily review of security events (Requirements 10.4 and 10.6)."PCI DSS v4.0" (PDF). PCI Security Standards Council. March 2022. Retrieved April 1, 2026. NIST Special Publication 800-53 addresses security information management through the AU (Audit and Accountability) control family, which specifies requirements for audit event generation, content, storage, and analysis."NIST SP 800-53 Rev. 5: Security and Privacy Controls". National Institute of Standards and Technology. September 2020. Retrieved April 1, 2026.
Imieliński–Lipski algebra
In database theory, Imieliński–Lipski algebra is an extension of relational algebra onto tables with different types of null values. It is used to operate on relations with incomplete information. Imieliński–Lipski algebras are defined to satisfy precise conditions for semantically meaningful extension of the usual relational operators, such as projection, selection, union, and join, from operators on relations to operators on relations with various kinds of "null values". These conditions require that the system be safe in the sense that no incorrect conclusion is derivable by using a specified subset F of the relational operators; and that it be complete in the sense that all valid conclusions expressible by relational expressions using operators in F are in fact derivable in this system. For example, it is well known that the three-valued logic approach to deal with null values, supported treatment of nulls values by SQL is not complete, see Ullman book. To show this, let T be: Take SQL query Q SQL query Q will return empty set (no results) under 3-valued semantics currently adopted by all variants of SQL. This is the case because in SQL, NULL is never equal to any constant – in this case, neither to “Spring” nor “Fall” nor “Winter” (if there is Winter semester in this school). NULL='Spring' will evaluate to MAYBE and so will NULL='Fall'. The disjunction MAYBE OR MAYBE evaluates to MAYBE (not TRUE). Thus Igor will not be part of the answer (and of course neither will Rohit). But Igor should be returned as the answer. Indeed, regardless what semester Igor took the Networks class (no matter what was the unknown value of NULL), the selection condition will be true. This “Igor” will be missed by SQL and the SQL answer would be incomplete according to completeness requirements specified in Tomasz Imieliński, Witold Lipski, 'Incomplete Information in Relational Databases'. It is also argued there that 3-valued logic (TRUE, FALSE, MAYBE) can never provide guarantee of complete answer for tables with incomplete information. Three algebras which satisfy conditions of safety and completeness are defined as Imielinski–Lipski algebras: the Codd-Tables algebra, the V-tables algebra and the Conditional tables (C-tables) algebra. == Codd-tables algebra == Codd-tables algebra is based on the usual Codd's single NULL values. The table T above is an example of Codd-table. Codd-table algebra supports projection and positive selections only. It is also demonstrated in [IL84 that it is not possible to correctly extend more relational operators over Codd-Tables. For example, such basic operation as join is not extendable over Codd-tables. It is not possible to define selections with Boolean conditions involving negation and preserve completeness. For example, queries like the above query Q cannot be supported. In order to be able to extend more relational operators, more expressive form of null value representation is needed in tables which are called V-table. == V-tables algebra == V-tables algebra is based on many different ("marked") null values or variables allowed to appear in a table. V-tables allow to show that a value may be unknown but the same for different tuples. For example, in the table below Gaurav and Igor order the same (but unknown) beer in two unknown bars (which may, or may not be different – but remain unknown). Gaurav and Jane frequent the same unknown bar (Y1). Thus, instead one NULL value, we use indexed variables, or Skolem constants . V-tables algebra is shown to correctly support projection, positive selection (with no negation occurring in the selection condition), union, and renaming of attributes, which allows for processing arbitrary conjunctive queries. A very desirable property enjoyed by the V-table algebra is that all relational operators on tables are performed in exactly the same way as in the case of the usual relations. === Conditional tables (c-tables) algebra === Example of conditional table (c-table) is shown below. It has additional column “con” which is a Boolean condition involving variables, null values – same as in V-tables. over the following table c-table Conditional tables algebra, mainly of theoretical interest, supports projection, selection, union, join, and renaming. Under closed-world assumption, it can also handle the operator of difference, thus it can support all relational operators. == History == Imieliński–Lipski algebras were introduced by Tomasz Imieliński and Witold Lipski Jr. in Incomplete Information in Relational Databases.
Resource Description Framework
The Resource Description Framework (RDF) is a method to describe and exchange graph data. It was originally designed as a data model for metadata by the World Wide Web Consortium (W3C). It provides a variety of syntax notations and formats, of which the most widely used is Turtle (Terse RDF Triple Language). RDF is a directed graph composed of triple statements. An RDF graph statement is represented by: (1) a node for the subject, (2) an arc from subject to object, representing a predicate, and (3) a node for the object. Each of these parts can be identified by a Internationalized Resource Identifier (IRI). An object can also be a literal value. This simple, flexible data model has a lot of expressive power to represent complex situations, relationships, and other things of interest, while also being appropriately abstract. RDF was adopted as a W3C recommendation in 1999. The RDF 1.0 specification was published in 2004, and the RDF 1.1 specification in 2014. SPARQL is a standard query language for RDF graphs. RDF Schema (RDFS), Web Ontology Language (OWL) and SHACL (Shapes Constraint Language) are ontology languages that are used to describe RDF data. == Overview == The RDF data model is similar to classical conceptual modeling approaches (such as entity–relationship or class diagrams). It is based on the idea of making statements about resources (in particular web resources) in expressions of the form subject–predicate–object, known as triples. The subject denotes the resource; the predicate denotes traits or aspects of the resource, and expresses a relationship between the subject and the object. For example, one way to represent the notion "The sky has the color blue" in RDF is as the triple: a subject denoting "the sky", a predicate denoting "has the color", and an object denoting "blue". Therefore, RDF uses subject instead of object (or entity) in contrast to the typical approach of an entity–attribute–value model in object-oriented design: entity (sky), attribute (color), and value (blue). RDF is an abstract model with several serialization formats (being essentially specialized file formats). In addition the particular encoding for resources or triples can vary from format to format. This mechanism for describing resources is a major component in the W3C's Semantic Web activity: an evolutionary stage of the World Wide Web in which automated software can store, exchange, and use machine-readable information distributed throughout the Web, in turn enabling users to deal with the information with greater efficiency and certainty. RDF's simple data model and ability to model disparate, abstract concepts has also led to its increasing use in knowledge management applications unrelated to Semantic Web activity. A collection of RDF statements intrinsically represents a labeled, directed multigraph. This makes an RDF data model better suited to certain kinds of knowledge representation than other relational or ontological models. As RDFS, OWL and SHACL demonstrate, one can build additional ontology languages upon RDF. == History == The initial RDF design, intended to "build a vendor-neutral and operating system- independent system of metadata", derived from the W3C's Platform for Internet Content Selection (PICS), an early web content labelling system, but the project was also shaped by ideas from Dublin Core, and from the Meta Content Framework (MCF), which had been developed during 1995 to 1997 by Ramanathan V. Guha at Apple and Tim Bray at Netscape. A first public draft of RDF appeared in October 1997, issued by a W3C working group that included representatives from IBM, Microsoft, Netscape, Nokia, Reuters, SoftQuad, and the University of Michigan. In 1999, the W3C published the first recommended RDF specification, the Model and Syntax Specification ("RDF M&S"). This described RDF's data model and an XML serialization. Two persistent misunderstandings about RDF developed at this time: firstly, due to the MCF influence and the RDF "Resource Description" initialism, the idea that RDF was specifically for use in representing metadata; secondly that RDF was an XML format rather than a data model, and only the RDF/XML serialisation being XML-based. RDF saw little take-up in this period, but there was significant work done in Bristol, around ILRT at Bristol University and HP Labs, and in Boston at MIT. RSS 1.0 and FOAF became exemplar applications for RDF in this period. The recommendation of 1999 was replaced in 2004 by a set of six specifications: "The RDF Primer", "RDF Concepts and Abstract", "RDF/XML Syntax Specification (revised)", "RDF Semantics", "RDF Vocabulary Description Language 1.0", and "The RDF Test Cases". This series was superseded in 2014 by the following six "RDF 1.1" documents: "RDF 1.1 Primer", "RDF 1.1 Concepts and Abstract Syntax", "RDF 1.1 XML Syntax", "RDF 1.1 Semantics", "RDF Schema 1.1", and "RDF 1.1 Test Cases". == RDF topics == === Vocabulary === The vocabulary defined by the RDF specification is as follows: ==== Classes ==== ===== rdf ===== rdf:XMLLiteral the class of XML literal values rdf:Property the class of properties rdf:Statement the class of RDF statements rdf:Alt, rdf:Bag, rdf:Seq containers of alternatives, unordered containers, and ordered containers (rdfs:Container is a super-class of the three) rdf:List the class of RDF Lists rdf:nil an instance of rdf:List representing the empty list ===== rdfs ===== rdfs:Resource the class resource, everything rdfs:Literal the class of literal values, e.g. strings and integers rdfs:Class the class of classes rdfs:Datatype the class of RDF datatypes rdfs:Container the class of RDF containers rdfs:ContainerMembershipProperty the class of container membership properties, rdf:_1, rdf:_2, ..., all of which are sub-properties of rdfs:member ==== Properties ==== ===== rdf ===== rdf:type an instance of rdf:Property used to state that a resource is an instance of a class rdf:first the first item in the subject RDF list rdf:rest the rest of the subject RDF list after rdf:first rdf:value idiomatic property used for structured values rdf:subject the subject of the RDF statement rdf:predicate the predicate of the RDF statement rdf:object the object of the RDF statement rdf:Statement, rdf:subject, rdf:predicate, rdf:object are used for reification (see below). ===== rdfs ===== rdfs:subClassOf the subject is a subclass of a class rdfs:subPropertyOf the subject is a subproperty of a property rdfs:domain a domain of the subject property rdfs:range a range of the subject property rdfs:label a human-readable name for the subject rdfs:comment a description of the subject resource rdfs:member a member of the subject resource rdfs:seeAlso further information about the subject resource rdfs:isDefinedBy the definition of the subject resource This vocabulary is used as a foundation for RDF Schema, where it is extended. === Serialization formats === Several common serialization formats are in use, including: Turtle, a compact, human-friendly format. TriG, an extension of Turtle to datasets. N-Triples, a very simple, easy-to-parse, line-based format that is not as compact as Turtle. N-Quads, a superset of N-Triples, for serializing multiple RDF graphs. JSON-LD, a JSON-based serialization. N3 or Notation3, a non-standard serialization that is very similar to Turtle, but has some additional features, such as the ability to define inference rules. RDF/XML, an XML-based syntax that was the first standard format for serializing RDF. RDF/JSON, an alternative syntax for expressing RDF triples using a simple JSON notation. RDF/XML is sometimes misleadingly called simply RDF because it was introduced among the other W3C specifications defining RDF and it was historically the first W3C standard RDF serialization format. However, it is important to distinguish the RDF/XML format from the abstract RDF model itself. Although the RDF/XML format is still in use, other RDF serializations are now preferred by many RDF users, both because they are more human-friendly, and because some RDF graphs are not representable in RDF/XML due to restrictions on the syntax of XML QNames. With a little effort, virtually any arbitrary XML may also be interpreted as RDF using GRDDL (pronounced 'griddle'), Gleaning Resource Descriptions from Dialects of Languages. RDF triples may be stored in a type of database called a triplestore. === Resource identification === The subject of an RDF statement is either a uniform resource identifier (URI) or a blank node, both of which denote resources. Resources indicated by blank nodes are called anonymous resources. They are not directly identifiable from the RDF statement. The predicate is a URI which also indicates a resource, representing a relationship. The object is a URI, blank node or a Unicode string literal. As of RDF 1.1 resources are identified by Internationalized Resource Identifiers (IRIs); IRIs are a generalization of URIs. In Semantic Web applications, and in re
Stewart Nelson
Stewart Nelson is an American mathematician and programmer from The Bronx who co-founded Systems Concepts. == Biography == From a young age, Nelson was tinkering with electronics, aided and abetted by his father who was a physicist that had become an engineer. Stewart attended Poughkeepsie High School, graduating in the spring of 1963. From his first few days of High School, Stewart displayed his talents for hacking the international telephone trunk lines, along with an uncanny skill for picking combination locks, although this was always done as innocent entertainment. He simply loved the challenge of seeing how quickly he could accomplish this feat. His quirky sense of humor was always visible, as was his disdain for any rule that got in the way of his gaining knowledge. Stewart was an inspiration to the school's Tech-elec Club, as well as a ringleader in the founding of the school's pirate radio station. Nelson enrolled at MIT in 1963 and quickly became known for hooking up the AI Lab's PDP-1 (and later the PDP-6) to the telephone network, making him one of the first phreakers. Nelson later accomplished other feats like hard-wiring additional instructions into the PDP-1. Nelson was hired by Ed Fredkin's Information International Inc. at the urging of Marvin Minsky to work on PDP-7 programs at the MIT Computer Science and Artificial Intelligence Laboratory. Nelson was known as a brilliant software programmer. He was influential in LISP, the assembly instructions for the Digital Equipment Corporation PDP, and a number of other systems. The group of young hackers was known for working on systems after hours. One night, Nelson and others decided to rewire MIT's PDP-1 as a prank. Later, Margaret Hamilton tried to use the DEC-supplied DECAL assembler on the machine and it crashed repeatedly.
Mistral AI
Mistral AI SAS (French: [mistʁal]) is a French artificial intelligence (AI) company, headquartered in Paris. Founded in 2023, it has open-weight large language models (LLMs), with both open-source and proprietary AI models. As of 2025 the company has a valuation of more than US$14 billion. == Namesake == The company is named after the mistral, a powerful, cold wind in southern France, a term which originates from the Occitan language. == History == Mistral AI was established in April 2023 by three French AI researchers, Arthur Mensch, Guillaume Lample and Timothée Lacroix. Mensch, an expert in advanced AI systems, is a former employee of Google DeepMind; Lample and Lacroix, meanwhile, are large-scale AI models specialists who had worked for Meta Platforms. The trio originally met during their studies at École Polytechnique. == Company operation == === Funding === In June 2023, the start-up carried out a first fundraising of €105 million ($117 million) with investors including the American fund Lightspeed Venture Partners, Eric Schmidt, Xavier Niel and JCDecaux. The valuation was then estimated by the Financial Times at €240 million ($267 million). On 10 December 2023, Mistral AI announced that it had raised €385 million ($428 million) as part of its second fundraising. This round of financing involves the Californian fund Andreessen Horowitz, BNP Paribas and the software publisher Salesforce. It was valued at over €2 billion. On 26 February 2024, Microsoft announced an investment of $16 million in Mistral AI. On 16 April 2024, reporting revealed that Mistral was in talks to raise €500 million, a deal that would more than double its current valuation to at least €5 billion. In June 2024, Mistral AI secured a €600 million ($645 million) funding round, increasing its valuation to €5.8 billion ($6.2 billion). Based on valuation, as of June 2024, the company was ranked fourth globally in the AI industry, and first outside the San Francisco Bay Area. In April 2025, Mistral AI announced a €100 million partnership with the shipping company CMA CGM. In August 2025, the Financial Times reported that Mistral was in talks to raise $1 billion at a $10 billion valuation. In September 2025, Bloomberg announced that Mistral AI has secured a €2 billion investment valuing it at €12 billion ($14 billion). This comes after $1.5 billion investment from Dutch company ASML, which owns 11% of Mistral. In February 2026, Mistral acquired Koyeb, a Paris-based AI startup. Later that month, Mistral AI announced a multi-year strategic partnership with Accenture to help enterprises deploy sovereign AI solutions at scale. In March 2026 Mistral raised $830 million in order to build new datacenters near Paris and in Sweden. == Services == On 19 November, 2024, the company announced updates for Le Chat (pronounced /lə ʃa/ in French, like the French word for "cat"). It added the ability to create images, using Black Forest Labs' Flux Pro model. On 6 February 2025, Mistral AI released Le Chat on iOS and Android mobile devices. Mistral AI also introduced a Pro subscription tier, priced at $14.99 per month, which provides access to more advanced models, unlimited messaging, and web browsing. At the end of May 2026, Le Chat was renamed Vibe, and new features were introduced at the same time. == Models == The following table lists the main model versions of Mistral, describing the significant changes included with each version: === Mistral 7B === Mistral AI claimed in the Mistral 7B release blog post that the model outperforms LLaMA 2 13B on all benchmarks tested, and is on par with LLaMA 34B on many benchmarks tested, despite having only 7 billion parameters, a small size compared to its competitors. === Mixtral 8x7B === Mistral AI claimed in 2023 that its model beat both LLaMA 70B, and GPT-3.5 in most benchmarks. In March 2024, research conducted by Patronus AI comparing performance of LLMs on a 100-question test with prompts to generate text from books protected under U.S. copyright law found that OpenAI's GPT-4, Mixtral, Meta AI's LLaMA-2, and Anthropic's Claude 2 generated copyrighted text verbatim in 44%, 22%, 10%, and 8% of responses respectively. === Mistral Small 3.1 === On 17 March 2025, Mistral released Mistral Small 3.1 as a smaller, more efficient model. === Mistral Medium 3 === On 7 May 2025, Mistral AI released Mistral Medium 3. === Magistral Small and Magistral Medium === On 10 June 2025, Mistral AI released their first AI reasoning models: Magistral Small (open-source), and Magistral Medium, models which are purported to have chain-of-thought capabilities. === Mistral Large 3 and Ministral 3 === On 2 December 2025, Mistral AI released Mistral Large 3, a sparse, mixture-of-experts model with 41 billion active parameters and 675 billion total parameters, and Ministral 3, three small, dense models with 3 billion, 7 billion and 14 billion parameters. === Devstral 2 and Devstral Small 2 === On 10 December 2025, Mistral AI released Devstral 2 and Devstral Small 2. Devstral Small 2, a 24B parameter model is claimed to achieve better performance at coding than Qwen 3 Coder Flash model which is a 30B parameter model.
Certified social engineering prevention specialist
Certified Social Engineering Prevention Specialist (CSEPS) is a social engineering security-awareness training and professional certification program originally developed by Kevin Mitnick and Alexis Kasperavičius. == Course structure == The original CSEPS program was structured as a multi-module corporate security-awareness course designed to teach employees, managers, and IT personnel how social engineers manipulate human behavior to bypass technical security systems. The curriculum combined case studies, psychological analysis, attack demonstrations, pretexting exercises, and operational security scenarios. The course materials described social engineering as the exploitation of "the human factor" in information security and argued that traditional technical defenses alone were insufficient to protect organizations from deception-based attacks. The training program was divided into instructional modules covering topics such as: social engineering methodology and threat analysis intelligence gathering and reconnaissance dumpster diving pretexting elicitation technique telephone-system exploitation and caller-ID spoofing psychological influence techniques industrial espionage identity theft organizational vulnerabilities security policy development and employee awareness training The course also analyzed historical and contemporary case studies involving information theft, corporate espionage, fraudulent wire transfers, and telephone-based impersonation attacks. Training exercises required participants to analyze how attackers established credibility, manipulated trust, overcame objections, and exploited organizational procedures. According to The Wall Street Journal, CSEPS was delivered as a two-day "boot camp" course costing approximately US$1,500 per attendee. Clients reportedly included the United States Air Force and the United States Marine Corps. The certification examination included multiple-choice and written-response sections dealing with social-engineering defense scenarios and mitigation strategies. == History == In 2003, Mitnick and Kasperavičius partnered with the Florida-based IT training company Intense School Inc. to offer CSEPS classes throughout the United States. In 2020, Mitnick partnered with security-awareness training company KnowBe4, and elements of the original CSEPS material became incorporated into KnowBe4's social-engineering awareness training offerings.
National Library of Medicine classification
The National Library of Medicine (NLM) classification system is a library indexing system covering the fields of medicine and preclinical basic sciences. Operated and maintained by the U.S. National Library of Medicine, the NLM classification is patterned after the Library of Congress (LC) Classification system: alphabetical letters denote broad subject categories which are subdivided by numbers. For example, QW 279 would indicate a book on an aspect of microbiology or immunology. The one- or two-letter alphabetical codes in the NLM classification use a limited range of letters: only QS–QZ and W–WZ. This allows the NLM system to co-exist with the larger LC coding scheme as neither of these ranges are used in the LC system. There are, however, three pre-existing codes in the LC system which overlap with the NLM: Human Anatomy (QM), Microbiology (QR), and Medicine (R). To avoid further confusion, these three codes are not used in the NLM. The headings for the individual schedules (letters or letter pairs) are given in brief form (e.g., QW - Microbiology and Immunology; WG - Cardiovascular System) and together they provide an outline of the subjects covered by the NLM classification. Headings are interpreted broadly and include the physiological system, the specialties connected with them, the regions of the body chiefly concerned and subordinate related fields. The NLM system is hierarchical, and within each schedule, division by organ usually has priority. Each main schedule, as well as some sub-sections, begins with a group of form numbers ranging generally from 1–49 which classify materials by publication type, e.g., dictionaries, atlases, laboratory manuals, etc. The main schedules QS-QZ, W-WY, and WZ (excluding the range WZ 220–270) classify works published after 1913; the 19th century schedule is used for works published 1801–1913; and WZ 220-270 is used to provide century groupings for works published before 1801. == Classification categories == === Preclinical Sciences === QS Human Anatomy QT Physiology QU Biochemistry QV Pharmacology QW Microbiology & Immunology QX Parasitology QY Clinical Pathology QZ Pathology === Medicine and Related Subjects === W Health Professions WA Public Health WB Practice of Medicine WC Communicable Diseases WD Disorders of Systemic, Metabolic, or Environmental Origin, etc. WE Musculoskeletal System WF Respiratory System WG Cardiovascular System WH Hemic and Lymphatic Systems WI Digestive System WJ Urogenital System WK Endocrine System WL Nervous System WM Psychiatry WN Radiology. Diagnostic Imaging WO Surgery WP Gynecology WQ Obstetrics WR Dermatology WS Pediatrics WT Geriatrics. Chronic Disease WU Dentistry. Oral Surgery WV Otolaryngology WW Ophthalmology WX Hospitals & Other Health Facilities WY Nursing WZ History of Medicine 19th Century Schedule