Are you over 18 and want to see adult content?
More Annotations
Folding@home – Fighting disease with a world wide distributed super computer.
Are you over 18 and want to see adult content?
Kraftquelle-Frankfurt am Main - Höchst - Startseite
Are you over 18 and want to see adult content?
A complete backup of lirik-lagu-kristen.blogspot.com
Are you over 18 and want to see adult content?
Futebol.com : Futebol ao vivo, palpites e resultados de jogos em directo
Are you over 18 and want to see adult content?
Favourite Annotations
Pós-graduação e MBA | UNIP - Universidade Paulista
Are you over 18 and want to see adult content?
A complete backup of sveinnyhus.blogspot.com
Are you over 18 and want to see adult content?
Ready Made Niche Websites - Turnkey Websites - Buy Niche Sites
Are you over 18 and want to see adult content?
TPM Rotator - Free Unlimited URL Rotator
Are you over 18 and want to see adult content?
Text
industry.
OBJECT-ORIENTED DATABASE MANAGEMENT SYSTEMS (OODBMS An object-oriented database management system (OODBMS, but sometimes just called “object database”) is a DBMS that stores data in a logical model that is closely aligned with an application program’s object model. Of course, an OODBMS will have a physical data model optimized for the kinds of logical data model it expects. EXAMPLES OF MACHINE-GENERATED DATA Examples of machine-generated data. Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Computer, network, and other equipment logs. Satellite and similar telemetry (whether for espionage or science) Location data such as RFID chip readings, GPS systemoutput, etc.
DIFFERENTIATION IN BUSINESS INTELLIGENCE Differentiation in business intelligence. Parts of the business intelligence differentiation story resemble the one I just posted for data management.After all: Both kinds of products query and aggregate data. Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists. INTRODUCTION TO SYNCSORT AND DMEXPRESS Introduction to Syncsort and DMExpress. Let’s start with some Syncsort basics. Syncsort was founded in 1968. As you might guess from its name and age, Syncsort started out selling software for IBM mainframes, used for sorting data. TERMINOLOGY: POLY-STRUCTURED DATA, DATABASES, AND DBMS Terminology: poly-structured data, databases, and DBMS. My recent argument that the common terms “unstructured data” and “semi-structured data” are misnomers, and that a word like “multi-” or “poly-structured”* would be better, seems to have been well-received.But which is it — “multi-” or “poly-“? *Everybody seems to like “poly-structured” better when it has a TECHNICAL BASICS OF SYBASE IQ Sybase IQ stores data in columns – like, for example, Vertica. Sybase IQ relies on indexes to retrieve data – unlike, for example, Vertica, in which the column pretty much is the index. However, columns themselves can be used as indexes in the usual Vertica -like way. Most of Sybase IQ’s indexes are bitmaps, or a lot like bitmaps,ala
RYW (READ-YOUR-WRITES) CONSISTENCY EXPLAINED Definition: RYW (Read-Your-Writes) consistency is achieved when the system guarantees that, once a record has been updated, any attempt to read the record will return the updated value. Here a “record” can be a row, a key-value pair, or any similar unit of data. An “update” can be whichever of insert/append or true change thesystem
MONGODB 3.4 AND "MULTIMODEL" QUERY MongoDB 3.4 and “multimodel” query “Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. THE WORKDAY ARCHITECTURE The Workday architecture — a new kind of OLTP software stack. One of my coolest company visits in some time was to SaaS (Software as a Service) vendor Workday, Inc., earlier this month. Reasons included: Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same. DBMS 2 : DATABASE MANAGEMENT AND ANALYTIC TECHNOLOGIES INABOUTMAPREDUCEZYNGAZOOMDATAMYSQLTABLEAU SOFTWARE DBMS 2 covers database management, analytics, and related technologies. Text Technologies covers text mining, search, and social software. Strategic Messaging analyzes marketing and messaging strategy. The Monash Report examines technology and public policy issues. Software Memories recounts the history of the softwareindustry.
OBJECT-ORIENTED DATABASE MANAGEMENT SYSTEMS (OODBMS An object-oriented database management system (OODBMS, but sometimes just called “object database”) is a DBMS that stores data in a logical model that is closely aligned with an application program’s object model. Of course, an OODBMS will have a physical data model optimized for the kinds of logical data model it expects. EXAMPLES OF MACHINE-GENERATED DATA Examples of machine-generated data. Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Computer, network, and other equipment logs. Satellite and similar telemetry (whether for espionage or science) Location data such as RFID chip readings, GPS systemoutput, etc.
DIFFERENTIATION IN BUSINESS INTELLIGENCE Differentiation in business intelligence. Parts of the business intelligence differentiation story resemble the one I just posted for data management.After all: Both kinds of products query and aggregate data. Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists. INTRODUCTION TO SYNCSORT AND DMEXPRESS Introduction to Syncsort and DMExpress. Let’s start with some Syncsort basics. Syncsort was founded in 1968. As you might guess from its name and age, Syncsort started out selling software for IBM mainframes, used for sorting data. TERMINOLOGY: POLY-STRUCTURED DATA, DATABASES, AND DBMS Terminology: poly-structured data, databases, and DBMS. My recent argument that the common terms “unstructured data” and “semi-structured data” are misnomers, and that a word like “multi-” or “poly-structured”* would be better, seems to have been well-received.But which is it — “multi-” or “poly-“? *Everybody seems to like “poly-structured” better when it has a TECHNICAL BASICS OF SYBASE IQ Sybase IQ stores data in columns – like, for example, Vertica. Sybase IQ relies on indexes to retrieve data – unlike, for example, Vertica, in which the column pretty much is the index. However, columns themselves can be used as indexes in the usual Vertica -like way. Most of Sybase IQ’s indexes are bitmaps, or a lot like bitmaps,ala
RYW (READ-YOUR-WRITES) CONSISTENCY EXPLAINED Definition: RYW (Read-Your-Writes) consistency is achieved when the system guarantees that, once a record has been updated, any attempt to read the record will return the updated value. Here a “record” can be a row, a key-value pair, or any similar unit of data. An “update” can be whichever of insert/append or true change thesystem
MONGODB 3.4 AND "MULTIMODEL" QUERY MongoDB 3.4 and “multimodel” query “Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. THE WORKDAY ARCHITECTURE The Workday architecture — a new kind of OLTP software stack. One of my coolest company visits in some time was to SaaS (Software as a Service) vendor Workday, Inc., earlier this month. Reasons included: Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same. STORAGE | DBMS 2 : DATABASE MANAGEMENT SYSTEM SERVICES Interana. Interana has an interesting story, in technology and business model alike. For starters: Interana does ad-hoc event series analytics, which they call “interactive behavioral analytics solutions”.; Interana has a full-stack analytic offering, include:TRANSRELATIONAL
Relational purists should root for ScaleDB. I just put up a long post about a small development-stage company, ScaleDB.The punchline is that ScaleDB has a data access method — an extension of Patricia tries — that gives referential integrity and updatable views for free.. People who think current “relational” DBMS aren’t relational enough often suggest that’s the kind of foundation DIFFERENTIATION IN BUSINESS INTELLIGENCE Differentiation in business intelligence. Parts of the business intelligence differentiation story resemble the one I just posted for data management.After all: Both kinds of products query and aggregate data. Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists. FIVE DIFFERENT KINDS OF BUSINESS INTELLIGENCE Five different kinds of business intelligence. Having recently categorized seven different kinds of database, let me now make a similar effort for business intelligence.To a first approximation, I’d like to split BI use cases into 2×2 = 4 groups, along twodimensions:
TERMINOLOGY: POLY-STRUCTURED DATA, DATABASES, AND DBMS Terminology: poly-structured data, databases, and DBMS. My recent argument that the common terms “unstructured data” and “semi-structured data” are misnomers, and that a word like “multi-” or “poly-structured”* would be better, seems to have been well-received.But which is it — “multi-” or “poly-“? *Everybody seems to like “poly-structured” better when it has a LIMITATIONS OF THE RELATIONAL MODEL Limitations of the Relational Model. In my October Computerworld column, I tried to explain some of the reasons why I don’t think the pure Relational Model should be as absolutely dominant as its most fervent proponents assert.. The key points were: 1. Logical and physical modeling will never be completely separable. 2. “True relational” DBMSs are very unlikely ever to be practically TEMPORAL DATA, TIME SERIES, AND IMPRECISE PREDICATES Temporal data, time series, and imprecise predicates. I’ve been confused about temporal data management for a while, because there are several different things going on. THE NETEZZA AND IBM DB2 APPROACHES TO COMPRESSION The Netezza and IBM DB2 approaches to compression. Thursday, I spent 3 hours talking with 10 of Netezza’s more senior engineers.Friday, I talked for 1 ½ hours with IBM Fellow and DB2 Chief Architect Tim Vincent, and we agreed we needed at least 2 hours more. "THE NETEZZA PRICE POINT" User data is a key price for me but user data is a confusing term. How much is actually business data. Assuming you could by a 6TB Exadata Machine and a 6TB Netezza server at the old $60k per TB price point and you fill the servers to the very top I calculate that Oracle costs $297k per TB of business data and Netezza costs $60k per TB ofbusiness data
COMPRESSION IN SYBASE ASE 15.7 15.7 turns out to be the first release of Sybase ASE with data compression. Sybase fondly believes that it is matching DB2 and leapfrogging Oracle in compression rate with a single compression scheme, namely page-level tokenization. More precisely, SAP and Sybase seem to believe that about compression rates for actual SAP application databases DBMS 2 : DATABASE MANAGEMENT AND ANALYTIC TECHNOLOGIES INABOUTMAPREDUCEZYNGAZOOMDATAMYSQLTABLEAU SOFTWARE DBMS 2 covers database management, analytics, and related technologies. Text Technologies covers text mining, search, and social software. Strategic Messaging analyzes marketing and messaging strategy. The Monash Report examines technology and public policy issues. Software Memories recounts the history of the softwareindustry.
OBJECT-ORIENTED DATABASE MANAGEMENT SYSTEMS (OODBMS An object-oriented database management system (OODBMS, but sometimes just called “object database”) is a DBMS that stores data in a logical model that is closely aligned with an application program’s object model. Of course, an OODBMS will have a physical data model optimized for the kinds of logical data model it expects. EXAMPLES OF MACHINE-GENERATED DATA Examples of machine-generated data. Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Computer, network, and other equipment logs. Satellite and similar telemetry (whether for espionage or science) Location data such as RFID chip readings, GPS systemoutput, etc.
FIVE DIFFERENT KINDS OF BUSINESS INTELLIGENCE Five different kinds of business intelligence. Having recently categorized seven different kinds of database, let me now make a similar effort for business intelligence.To a first approximation, I’d like to split BI use cases into 2×2 = 4 groups, along twodimensions:
INTRODUCTION TO SYNCSORT AND DMEXPRESS Introduction to Syncsort and DMExpress. Let’s start with some Syncsort basics. Syncsort was founded in 1968. As you might guess from its name and age, Syncsort started out selling software for IBM mainframes, used for sorting data. TECHNICAL BASICS OF SYBASE IQ Sybase IQ stores data in columns – like, for example, Vertica. Sybase IQ relies on indexes to retrieve data – unlike, for example, Vertica, in which the column pretty much is the index. However, columns themselves can be used as indexes in the usual Vertica -like way. Most of Sybase IQ’s indexes are bitmaps, or a lot like bitmaps,ala
RYW (READ-YOUR-WRITES) CONSISTENCY EXPLAINED Definition: RYW (Read-Your-Writes) consistency is achieved when the system guarantees that, once a record has been updated, any attempt to read the record will return the updated value. Here a “record” can be a row, a key-value pair, or any similar unit of data. An “update” can be whichever of insert/append or true change thesystem
TERMINOLOGY: POLY-STRUCTURED DATA, DATABASES, AND DBMS Terminology: poly-structured data, databases, and DBMS. My recent argument that the common terms “unstructured data” and “semi-structured data” are misnomers, and that a word like “multi-” or “poly-structured”* would be better, seems to have been well-received.But which is it — “multi-” or “poly-“? *Everybody seems to like “poly-structured” better when it has a "THE NETEZZA PRICE POINT" User data is a key price for me but user data is a confusing term. How much is actually business data. Assuming you could by a 6TB Exadata Machine and a 6TB Netezza server at the old $60k per TB price point and you fill the servers to the very top I calculate that Oracle costs $297k per TB of business data and Netezza costs $60k per TB ofbusiness data
THE WORKDAY ARCHITECTURE The Workday architecture — a new kind of OLTP software stack. One of my coolest company visits in some time was to SaaS (Software as a Service) vendor Workday, Inc., earlier this month. Reasons included: Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same. DBMS 2 : DATABASE MANAGEMENT AND ANALYTIC TECHNOLOGIES INABOUTMAPREDUCEZYNGAZOOMDATAMYSQLTABLEAU SOFTWARE DBMS 2 covers database management, analytics, and related technologies. Text Technologies covers text mining, search, and social software. Strategic Messaging analyzes marketing and messaging strategy. The Monash Report examines technology and public policy issues. Software Memories recounts the history of the softwareindustry.
OBJECT-ORIENTED DATABASE MANAGEMENT SYSTEMS (OODBMS An object-oriented database management system (OODBMS, but sometimes just called “object database”) is a DBMS that stores data in a logical model that is closely aligned with an application program’s object model. Of course, an OODBMS will have a physical data model optimized for the kinds of logical data model it expects. EXAMPLES OF MACHINE-GENERATED DATA Examples of machine-generated data. Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Computer, network, and other equipment logs. Satellite and similar telemetry (whether for espionage or science) Location data such as RFID chip readings, GPS systemoutput, etc.
FIVE DIFFERENT KINDS OF BUSINESS INTELLIGENCE Five different kinds of business intelligence. Having recently categorized seven different kinds of database, let me now make a similar effort for business intelligence.To a first approximation, I’d like to split BI use cases into 2×2 = 4 groups, along twodimensions:
INTRODUCTION TO SYNCSORT AND DMEXPRESS Introduction to Syncsort and DMExpress. Let’s start with some Syncsort basics. Syncsort was founded in 1968. As you might guess from its name and age, Syncsort started out selling software for IBM mainframes, used for sorting data. TECHNICAL BASICS OF SYBASE IQ Sybase IQ stores data in columns – like, for example, Vertica. Sybase IQ relies on indexes to retrieve data – unlike, for example, Vertica, in which the column pretty much is the index. However, columns themselves can be used as indexes in the usual Vertica -like way. Most of Sybase IQ’s indexes are bitmaps, or a lot like bitmaps,ala
RYW (READ-YOUR-WRITES) CONSISTENCY EXPLAINED Definition: RYW (Read-Your-Writes) consistency is achieved when the system guarantees that, once a record has been updated, any attempt to read the record will return the updated value. Here a “record” can be a row, a key-value pair, or any similar unit of data. An “update” can be whichever of insert/append or true change thesystem
TERMINOLOGY: POLY-STRUCTURED DATA, DATABASES, AND DBMS Terminology: poly-structured data, databases, and DBMS. My recent argument that the common terms “unstructured data” and “semi-structured data” are misnomers, and that a word like “multi-” or “poly-structured”* would be better, seems to have been well-received.But which is it — “multi-” or “poly-“? *Everybody seems to like “poly-structured” better when it has a "THE NETEZZA PRICE POINT" User data is a key price for me but user data is a confusing term. How much is actually business data. Assuming you could by a 6TB Exadata Machine and a 6TB Netezza server at the old $60k per TB price point and you fill the servers to the very top I calculate that Oracle costs $297k per TB of business data and Netezza costs $60k per TB ofbusiness data
THE WORKDAY ARCHITECTURE The Workday architecture — a new kind of OLTP software stack. One of my coolest company visits in some time was to SaaS (Software as a Service) vendor Workday, Inc., earlier this month. Reasons included: Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same.TRANSRELATIONAL
Relational purists should root for ScaleDB. I just put up a long post about a small development-stage company, ScaleDB.The punchline is that ScaleDB has a data access method — an extension of Patricia tries — that gives referential integrity and updatable views for free.. People who think current “relational” DBMS aren’t relational enough often suggest that’s the kind of foundation FIVE DIFFERENT KINDS OF BUSINESS INTELLIGENCE Five different kinds of business intelligence. Having recently categorized seven different kinds of database, let me now make a similar effort for business intelligence.To a first approximation, I’d like to split BI use cases into 2×2 = 4 groups, along twodimensions:
DIFFERENTIATION IN BUSINESS INTELLIGENCE Differentiation in business intelligence. Parts of the business intelligence differentiation story resemble the one I just posted for data management.After all: Both kinds of products query and aggregate data. Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists. TERMINOLOGY: POLY-STRUCTURED DATA, DATABASES, AND DBMS Terminology: poly-structured data, databases, and DBMS. My recent argument that the common terms “unstructured data” and “semi-structured data” are misnomers, and that a word like “multi-” or “poly-structured”* would be better, seems to have been well-received.But which is it — “multi-” or “poly-“? *Everybody seems to like “poly-structured” better when it has a LIMITATIONS OF THE RELATIONAL MODEL Limitations of the Relational Model. In my October Computerworld column, I tried to explain some of the reasons why I don’t think the pure Relational Model should be as absolutely dominant as its most fervent proponents assert.. The key points were: 1. Logical and physical modeling will never be completely separable. 2. “True relational” DBMSs are very unlikely ever to be practically MONGODB 3.4 AND "MULTIMODEL" QUERY MongoDB 3.4 and “multimodel” query “Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. NOSQL VS. NEWSQL VS. TRADITIONAL RDBMS NoSQL products commonly make sense for new applications. NewSQL products, to date, have had a harder time crossing that bar. The chief reasons for the difference are, I think: Programming model! Earlier to do a good and differentiated job in scale-out. Earlier to be at leastsomewhat mature.
THE NETEZZA AND IBM DB2 APPROACHES TO COMPRESSION The Netezza and IBM DB2 approaches to compression. Thursday, I spent 3 hours talking with 10 of Netezza’s more senior engineers.Friday, I talked for 1 ½ hours with IBM Fellow and DB2 Chief Architect Tim Vincent, and we agreed we needed at least 2 hours more. SOCIAL NETWORK ANALYSIS, AKA RELATIONSHIP ANALYTICS Social network analysis, aka relationship analytics. A number of applications lend themselves to graph-oriented analytics, including: Finding bad guys (national intelligence) Finding bad guys (anti-fraud) Data mining the social graph (e.g., for advertising optimization on social networks, or to identify influencers) There are plenty moregraph
COMPRESSION IN SYBASE ASE 15.7 15.7 turns out to be the first release of Sybase ASE with data compression. Sybase fondly believes that it is matching DB2 and leapfrogging Oracle in compression rate with a single compression scheme, namely page-level tokenization. More precisely, SAP and Sybase seem to believe that about compression rates for actual SAP application databases DBMS 2 : DATABASE MANAGEMENT AND ANALYTIC TECHNOLOGIES INABOUTMAPREDUCEZYNGAZOOMDATAMYSQLTABLEAU SOFTWARE DBMS 2 covers database management, analytics, and related technologies. Text Technologies covers text mining, search, and social software. Strategic Messaging analyzes marketing and messaging strategy. The Monash Report examines technology and public policy issues. Software Memories recounts the history of the softwareindustry.
EXAMPLES OF MACHINE-GENERATED DATA Examples of machine-generated data. Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Computer, network, and other equipment logs. Satellite and similar telemetry (whether for espionage or science) Location data such as RFID chip readings, GPS systemoutput, etc.
INTRODUCTION TO SYNCSORT AND DMEXPRESS Introduction to Syncsort and DMExpress. Let’s start with some Syncsort basics. Syncsort was founded in 1968. As you might guess from its name and age, Syncsort started out selling software for IBM mainframes, used for sorting data. DIFFERENTIATION IN BUSINESS INTELLIGENCE Differentiation in business intelligence. Parts of the business intelligence differentiation story resemble the one I just posted for data management.After all: Both kinds of products query and aggregate data. Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists. LIMITATIONS OF THE RELATIONAL MODEL Limitations of the Relational Model. In my October Computerworld column, I tried to explain some of the reasons why I don’t think the pure Relational Model should be as absolutely dominant as its most fervent proponents assert.. The key points were: 1. Logical and physical modeling will never be completely separable. 2. “True relational” DBMSs are very unlikely ever to be practically TECHNICAL BASICS OF SYBASE IQ Sybase IQ stores data in columns – like, for example, Vertica. Sybase IQ relies on indexes to retrieve data – unlike, for example, Vertica, in which the column pretty much is the index. However, columns themselves can be used as indexes in the usual Vertica -like way. Most of Sybase IQ’s indexes are bitmaps, or a lot like bitmaps,ala
MONGODB 3.4 AND "MULTIMODEL" QUERY MongoDB 3.4 and “multimodel” query “Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. THE WORKDAY ARCHITECTURE The Workday architecture — a new kind of OLTP software stack. One of my coolest company visits in some time was to SaaS (Software as a Service) vendor Workday, Inc., earlier this month. Reasons included: Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same. "THE NETEZZA PRICE POINT" User data is a key price for me but user data is a confusing term. How much is actually business data. Assuming you could by a 6TB Exadata Machine and a 6TB Netezza server at the old $60k per TB price point and you fill the servers to the very top I calculate that Oracle costs $297k per TB of business data and Netezza costs $60k per TB ofbusiness data
TRANSRELATIONAL(TM) NONSENSE TransRelational (TM) nonsense. Database guru Christopher J. Date is apparently accepting money from attendees to his seminars on TransRelational (TM) database archicture, so that he can tell them about an as-yet unreleased product from Required DBMS 2 : DATABASE MANAGEMENT AND ANALYTIC TECHNOLOGIES INABOUTMAPREDUCEZYNGAZOOMDATAMYSQLTABLEAU SOFTWARE DBMS 2 covers database management, analytics, and related technologies. Text Technologies covers text mining, search, and social software. Strategic Messaging analyzes marketing and messaging strategy. The Monash Report examines technology and public policy issues. Software Memories recounts the history of the softwareindustry.
EXAMPLES OF MACHINE-GENERATED DATA Examples of machine-generated data. Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Computer, network, and other equipment logs. Satellite and similar telemetry (whether for espionage or science) Location data such as RFID chip readings, GPS systemoutput, etc.
INTRODUCTION TO SYNCSORT AND DMEXPRESS Introduction to Syncsort and DMExpress. Let’s start with some Syncsort basics. Syncsort was founded in 1968. As you might guess from its name and age, Syncsort started out selling software for IBM mainframes, used for sorting data. DIFFERENTIATION IN BUSINESS INTELLIGENCE Differentiation in business intelligence. Parts of the business intelligence differentiation story resemble the one I just posted for data management.After all: Both kinds of products query and aggregate data. Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists. LIMITATIONS OF THE RELATIONAL MODEL Limitations of the Relational Model. In my October Computerworld column, I tried to explain some of the reasons why I don’t think the pure Relational Model should be as absolutely dominant as its most fervent proponents assert.. The key points were: 1. Logical and physical modeling will never be completely separable. 2. “True relational” DBMSs are very unlikely ever to be practically TECHNICAL BASICS OF SYBASE IQ Sybase IQ stores data in columns – like, for example, Vertica. Sybase IQ relies on indexes to retrieve data – unlike, for example, Vertica, in which the column pretty much is the index. However, columns themselves can be used as indexes in the usual Vertica -like way. Most of Sybase IQ’s indexes are bitmaps, or a lot like bitmaps,ala
MONGODB 3.4 AND "MULTIMODEL" QUERY MongoDB 3.4 and “multimodel” query “Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. THE WORKDAY ARCHITECTURE The Workday architecture — a new kind of OLTP software stack. One of my coolest company visits in some time was to SaaS (Software as a Service) vendor Workday, Inc., earlier this month. Reasons included: Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same. "THE NETEZZA PRICE POINT" User data is a key price for me but user data is a confusing term. How much is actually business data. Assuming you could by a 6TB Exadata Machine and a 6TB Netezza server at the old $60k per TB price point and you fill the servers to the very top I calculate that Oracle costs $297k per TB of business data and Netezza costs $60k per TB ofbusiness data
TRANSRELATIONAL(TM) NONSENSE TransRelational (TM) nonsense. Database guru Christopher J. Date is apparently accepting money from attendees to his seminars on TransRelational (TM) database archicture, so that he can tell them about an as-yet unreleased product from Required DBMS 2 : DATABASE MANAGEMENT AND ANALYTIC TECHNOLOGIES IN DBMS 2 covers database management, analytics, and related technologies. Text Technologies covers text mining, search, and social software. Strategic Messaging analyzes marketing and messaging strategy. The Monash Report examines technology and public policyissues.
TRANSRELATIONAL
Relational purists should root for ScaleDB. I just put up a long post about a small development-stage company, ScaleDB.The punchline is that ScaleDB has a data access method — an extension of Patricia tries — that gives referential integrity and updatable views for free.. People who think current “relational” DBMS aren’t relational enough often suggest that’s the kind of foundation OBJECT-ORIENTED DATABASE MANAGEMENT SYSTEMS (OODBMS An object-oriented database management system (OODBMS, but sometimes just called “object database”) is a DBMS that stores data in a logical model that is closely aligned with an application program’s object model. Of course, an OODBMS will have a physical data model optimized for the kinds of logical data model it expects. INTRODUCTION TO SENSAGE SenSage says that, among analytic DBMS vendors, it competes with Oracle, IBM, Teradata, Netezza and, to some extent, Vertica and Greenplum. Technical SenSage highlights include: SenSage’s core technology is an append-only columnar DBMS, with no master node. SenSage’s DBMS uses no indexes and requires “no” databaseadministration.
FIVE DIFFERENT KINDS OF BUSINESS INTELLIGENCE Five different kinds of business intelligence. Having recently categorized seven different kinds of database, let me now make a similar effort for business intelligence.To a first approximation, I’d like to split BI use cases into 2×2 = 4 groups, along twodimensions:
SNOWFLAKE COMPUTING
Snowflake is offering an analytic DBMS on a SaaS (Software as a Service) basis. The Snowflake DBMS is built from scratch (as opposed, to for example, being based on PostgreSQL or Hadoop). The Snowflake DBMS is columnar and append-only, as has become common for analytic RDBMS. Snowflake claims excellent SQL coverage for a 1.0 product. ENTITY-CENTRIC EVENT SERIES ANALYTICS Entity-centric event series analytics. Much of modern analytic technology deals with what might be called an entity-centric sequence of events. For example: You receive and open various emails. You click on and look at various web sites and pages. Specific elements are displayed on those pages. You study various products, and even buysome.
READINGS IN DATABASE SYSTEMS I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. NOSQL VS. NEWSQL VS. TRADITIONAL RDBMS NoSQL products commonly make sense for new applications. NewSQL products, to date, have had a harder time crossing that bar. The chief reasons for the difference are, I think: Programming model! Earlier to do a good and differentiated job in scale-out. Earlier to be at leastsomewhat mature.
"THE NETEZZA PRICE POINT" User data is a key price for me but user data is a confusing term. How much is actually business data. Assuming you could by a 6TB Exadata Machine and a 6TB Netezza server at the old $60k per TB price point and you fill the servers to the very top I calculate that Oracle costs $297k per TB of business data and Netezza costs $60k per TB ofbusiness data
* Home
* About
* Contact
* Feeds
June 27, 2019
POLITICAL ISSUES AROUND BIG TECH COMPANIES The technology industry has an increasingly complex relationship to government and politics, most importantly in three areas: * Privacy and surveillance.* Censorship.
* Antitrust, general economic regulation, and other competitionmanagement.
Here’s some of what I think about that, plus links to a lot more. 1. For a long time, I’ve maintained: * Privacy and surveillance are very big deals. * Ultimately, they cannot be handled effectively without direct regulation of specific permitted and forbidden uses of data. The first point is now widely accepted. The second unfortunately is not; laws and regulations generally state who may or may not record, keep or decrypt particular kinds of data, rather than what particular uses they may make of it. 2. Another threat to freedom has arisen as big as that from privacy/surveillance: a many-fronts push for censorship. It would ultimately be calamitous for free countries to agree that the threat of “Fake News” and other dangerous online partisanship justifies general censorship, by governments or “platform” tech companies as the case may be, yet that is exactly the path we seem to be on. Fortunately, there are less dangerous waysto address
the same challenges. I expect to make as much fuss about this issue in the upcoming decade as I have about privacy/surveillance over the pastone.
Read more
Categories: Surveillance and privacy3 Comments
June 27, 2019
HOW TO BEAT “FAKE NEWS” Most observers hold several or all of the views: * “Fake news” and the like are severe problems. * Algorithmic solutions have not worked well to date. * Neither have manual ones. * Trusting governments to censor is a bad idea. * In light of the previous points, trusting large social media corporations to censor is a bad idea too. * Educating consumers to evaluate news and opinions accurately wouldbe … difficult.
And further:
* Whatever you think of the job traditional journalistic organizations previously did as news arbiters, they can’t do it as well anymore, for a variety of economic, structural and societalreasons.
But despite all those difficulties, I also believe that A GOOD SOLUTION TO NEWS/OPINION FILTERING IS FEASIBLE; it just can’t be as simple as everybody would like.Read more
Categories: Public policy, Text
1 Comment
June 25, 2018
NEW LEGAL LIMITS ON SURVEILLANCE IN THE US _The United States has new legal limits on electronic surveillance, both in one specific way and — more important — in prevailing judicial theory. This falls far short of the protections we ultimately need, but it’s a welcome development even so._ The recent Supreme Court case _Carpenter v. United States_ is a big deal. Let me start by saying: * Most fundamentally, the _Carpenter_ decision was BASED ON AND IMPLICITLY REAFFIRMS THE KATZ TEST.* This is good. * The _Carpenter_ decision UNDERMINES THE THIRD-PARTY DOCTRINE.** This is great. Strict adherence to the third-party doctrine would eventually have given the government unlimited rights of Orwelliansurveillance.
* The _Carpenter_ decision suggests the Court has adopted an equilibrium-adjustment approach to Fourth Amendment jurisprudence. * The “equilibrium” being maintained here is the balance between governmental rights to intrude on privacy and citizens’ rights notto be intruded on.
* e., equilibrium-adjustment is a COMMITMENT TO MAINTAINING APPROXIMATELY THE SAME LEVEL OF LIBERTY (WITH RESPECT TO SURVEILLANCE) WE’VE HAD ALL ALONG. * I got the equilibrium-adjustment point from Eugene Volokh’s excellent overview of the _Carpenter _decision.__
_*The Katz test
basically says that that an individual’s right to privacy is whatever society regards as a reasonable expectation of privacy atthat time._
_**The third-party doctrinebasically says
that any information of yours given voluntarily to a third party isn’t private. This includes transactional information such as purchases or telephone call detail records (CDRs)_ Key specifics include: Read more Categories: GIS and geospatial,
Surveillance and privacy1 Comment
June 20, 2018
BRITTLENESS, MURPHY’S LAW, AND SINGLE-IMPETUS FAILURES In my initial post on brittleness I suggested that a typical process is: * Build something brittle. * Strengthen it over time. In many engineering scenarios, a fuller description could be: * Design something that works in the base cases. * Anticipate edge cases and sources of error, and design for themtoo.
* Implement the design. * Discover which edge cases and error sources you failed toconsider.
* Improve your product to handle them too.* Repeat as needed.
So it’s necesseary to understand what is or isn’t likely to go wrong. Unfortunately, that need isn’t always met. Read more Categories: Analytic technologies, Text
4 Comments
June 20, 2018
BRITTLENESS AND INCREMENTAL IMPROVEMENT Every system — computer or otherwise — needs to deal with possibilities of damage or error. If it does this well, it may be regarded as “ROBUST”, “MATURE(D), “STRENGTHENED”, or simply “IMPROVED”.* Otherwise, it can reasonably be called “BRITTLE”. _*It’s also common to use the word “harden(ed)”. But I think that’s a poor choice, as brittle things are often also hard._ 0. As a general rule in IT: * New technologies and products are brittle. * They are strengthened INCREMENTALLY over time. There are many categories of IT strengthening. Two of the broadestare:
* Bug-fixing.
* Bottleneck Whack-A-Mole.
1. One of my more popular postsstated:
> Developing a good DBMS requires 5-7 years and tens of millions of> dollars.
The reasons I gave all spoke to brittleness/strengthening, mostobviously in:
> Those minor edge cases in which your Version 1 product works poorly > aren’t minor after all. Similar things are true for other kinds of “platform software” or distributed systems. 2. The UI brittleness/improvement story starts similarly: Read more Categories: Analytic technologies, Buying
processes , Theory
and architecture
1 Comment
May 20, 2018
TECHNOLOGY IMPLICATIONS OF POLITICAL TRENDS The tech industry has a broad range of political concerns. While I may complain that things have been a bit predictable in other respects, politics is having real and new(ish) technical consequences. In some cases, existing technology is clearly adequate to meet regulators’ and customers’ demands. Other needs look more like open research challenges. _1. PRIVACY REGULATIONS WILL BE VERY DIFFERENT IN DIFFERENT COUNTRIES OR REGIONS._ For starters: * This is one case in which the European Union’s bureaucracy is working pretty well. It’s making rules for the whole region, and they aren’t totally crazy ones. * Things are more chaotic in the English-speaking democracies. * Authoritarian regimes are enacting anti-privacy rules. All of these rules are subject to change based on: * Genuine technological change. * Changes in politicians’ or the public’s perceptions. And so I believe: FOR ANY MULTINATIONAL ORGANIZATION THAT HANDLES CUSTOMER DATA, PRIVACY/SECURITY REQUIREMENTS ARE LIKELY TO CHANGE CONSTANTLY. Technology decisions need to reflect that reality. _2._ _DATA SOVEREIGNTY/GEO-COMPLIANCEIS
A BIG DEAL._ In fact, this is one area where the EU and authoritarian countries such as Russia formally agree. Each wants its citizens’ data to be STORED LOCALLY, so as to ensure adherence to local privacyrules.
For raw, granular data, that’s a straightforward — even if annoying — requirement to meet. But things get murkier for data that is aggregated or otherwise derived.
Read more
Categories: Derived data,
Public policy
7 Comments
May 20, 2018
SOME STUFF THAT’S ALWAYS ON MY MIND I have a LOT of partially-written blog posts, but am struggling to get any of them finished (obviously). Much of the problem is that they have so many dependencies on each other. Clearly, then, I should consider refactoring my writing plans. So let’s start with this. Here, in no particular order, is a list of some things that I’ve said in the past, and which I still think are or should be of interest today. It’s meant to be background for numerous posts I write in the near future, and indeed a few hooks for such posts are included below. 1. DATA(BASE) MANAGEMENT technology is progressing pretty much as Iexpected.
* Vendors generally recognize that maturing a data store is an important, many-years-longprocess.
* Multiple kinds of data model are viable…
* … but it’s usually helpful to be able to do some kind of JOIN. * To deal with the variety of hardware/network/storage arrangements out there, layering/tiering is on the rise. (An amazing number of vendors each seem to think theyinvented the idea.)
2. Rightly or wrongly, enterprises are often quite sloppy aboutANALYTIC ACCURACY.
* My two central examples have long been inaccurate metrics and false-positive alerts.
* In predictive analytics, it’s straightforward to quantify how much additional value you’re leaving on the table with yourimperfect accuracy.
* Enterprise search and other text technologies are still oftenterrible.
* After years of “real-time” overhype, organizations have seemingly swung to under-valuing real-time analytics.
Read more
Categories: Data models and architecture,
Database diversity
,
Predictive modeling and advanced analytics,
Public policy
, Theory and
architecture
3 Comments
February 7, 2018
SOME THINGS I THINK ABOUT POLITICS When one tries to think comprehensively about politics these days, it quickly gets overwhelming. But I
think I’ve got some pieces of the puzzle figured out. Here they are in extremely summarized form. I’ll flesh them out later as seems tomake sense.
1. Most of what people are saying about modern TRIBALISM is correct. But PARTISANSHIP is not as absolute as some fear. In particular: * There are populist concerns on the right and left alike. * Partisans of all sides can be concerned about privacy, surveillance and government overreach.
2. The threat from TRUMP and his REPUBLICAN ENABLERS is indeed as bad as people fear. He’s a major danger to do terrible, irreversible harm to the US and the rest of the world. To date the irreversible damage hasn’t been all that terrible, but if Trump and his enablers are given enough time, the oldest modern democracy will be no more. All common interests notwithstanding, beating Trump’s supporters at the polls is of paramount importance. 3. I agree with those who claim that many of our problems stem from the shredding of TRUST. But few people seem to realize just how many different aspects of “trust” there are, nor how many degrees there can be of TRUSTWORTHINESS. It’s not just a binary choice between “honest servant of the people” and “lying, cheating crook”. _These observations have strong analogies in IT. What does it mean for a system to be “reliable” or to produce “accurate” results? There are many possible answers, each reasonable in differentcontexts._
Read more
Categories: Public policy,
Surveillance and privacy2 Comments
February 7, 2018
POLITICS CAN BE OVERWHELMING Like many people, I’ve been shocked and saddened by recent political developments. What I’ve done about it includes (but is not limitedto):
* _VENTED, RANTED AND SO ON._ That’s somewhat therapeutic, and also let me engage the other side and try to understand a little better how they think. * _TRIED TO UNDERSTAND WHAT’S HAPPENING._ I probably have had more available time to do that than most people. I also have a variety of relevant experiences to bring to bear. * _NEGLECTED MY WORK SOMEWHAT WHILE DOING ALL THAT. _This neglect has now stopped. After all, the future is quite uncertain, so we should probably work hard in the present while business is still good. * _WRITTEN UP SOME OF WHAT I’VE FIGURED OUT._ Of course. That’s what I do. But it’s only “some”, because … well, the entirety of politics is overwhelming. * _TRIED TO FIND SPECIFIC, ACTIONABLE WAYS TO HELP._ Stay tuned formore on that part.
As for those writings: Read more Categories: Public policy,
Surveillance and privacy2 Comments
January 22, 2018
THE CHAOTIC POLITICS OF PRIVACY Almost nobody pays attention to the real issues in privacy andsurveillance
.
That’s gotten only slightly better over the decade that I’ve written about the subject. But the problems with privacy/surveillance politics run yet deeper than that._WORLDWIDE_
The politics of PRIVACY AND SURVEILLANCE are confused, in many countries around the world. This is hardly surprising. After all: * Privacy involves complex technological issues. Few governments understand those well. * Privacy also involves complex business issues. Few governments understand those well either. * Citizen understanding of these issues is no better. Technical cluelessness isn’t the only problem. Privacy issues are commonly framed in terms of CIVIL LIBERTIES, NATIONAL SECURITY, LAW ENFORCEMENT and/or general NATIONAL SOVEREIGNTY. And these categories are inherently confusing, in that: * Opinions about them often cross standard partisan lines. * Different countries take very different approaches, especially in the “civil liberties” area. * These categories are rife with questionably-founded fears, such as supposed threats from terrorism, child pornographers, or “foreigninterference”.
DATA SOVEREIGNTY regulations — which are quite a big part of privacy law — get their own extra bit of confusion, because of the various purposes they can serve. Chief among these are: Read more Categories: Surveillance and privacy4 Comments
Next Page →
Subscribe to the Monash Research feed via RSSor
email:
Login
SEARCH OUR BLOGS AND WHITE PAPERS MONASH RESEARCH BLOGS * DBMS 2 covers database management, analytics, and related technologies. * Text Technologies covers text mining, search, and social software. * Strategic Messaging analyzes marketing and messaging strategy. * The Monash Report examines technology and public policy issues. * Software Memories recounts the history of the software industry.USER CONSULTING
Building a short list? Refining your strategic plan? We can help.VENDOR ADVISORY
We tell vendors what's happening -- and, more important, what they should do about it . MONASH RESEARCH HIGHLIGHTS Learn about white papers, webcasts, and blog highlights, by RSS oremail .
*
RECENT POSTS
* Political issues around big tech companies * How to beat “fake news” * New legal limits on surveillance in the US * Brittleness, Murphy’s Law, and single-impetus failures * Brittleness and incremental improvement*
CATEGORIES
* About this blog
* Analytic glossary
* Analytic technologies * Business intelligence * Data mart outsourcing* Data warehousing
* MOLAP
* Predictive modeling and advanced analytics* Application areas
* Games and virtual worlds* Health care
* Investment research and trading* Log analysis
* Scientific research * Telecommunications* Web analytics
* Buying processes
* Benchmarks and POCs * Companies and products* 1010data
* Ab Initio Software* Actian and Ingres
* Aerospike
* Akiban
* Aleri and Coral8
* Algebraix
* Alpha Five
* Amazon and its cloud* ANTs Software
* Aster Data
* Ayasdi
* Basho and Riak
* Business Objects
* Calpont
* Cassandra
* Cast Iron Systems
* Cirro
* Citus Data
* ClearStory Data
* Cloudant
* Cloudera
* Clustrix
* Cogito and 7 Degrees* Cognos
* Continuent
* Couchbase
* CouchDB
* Databricks, Spark and BDAS* DATAllegro
* Datameer
* DataStax
* Dataupia
* dbShards and CodeFutures* Elastra
* EMC
* Endeca
* EnterpriseDB and Postgres Plus* Exasol
* Expressor
* FileMaker
* GenieDB
* Gooddata
* Greenplum
* Groovy Corporation* Hadapt
* Hadoop
* HBase
* Hortonworks
* HP and Neoview
* IBM and DB2
* pureXML
* illuminate Solutions* Infobright
* Informatica
* Information Builders* Inforsense
* Intel
* Intersystems and Cache'* Jaspersoft
* Kafka and Confluent* Kalido
* Kaminario
* Kickfire
* Kognitio
* KXEN
* MapR
* MarkLogic
* McObject
* memcached
* MemSQL
* Metamarkets and Druid * Microsoft and SQL*Server* MicroStrategy
* MonetDB
* MongoDB
* MySQL
* Neo Technology and Neo4j* Netezza
* NuoDB
* Nutonian
* Objectivity and Infinite Graph* Oracle
* Exadata
* Oracle TimesTen
* ParAccel
* Pentaho
* Pervasive Software* PivotLink
* Platfora
* PostgreSQL
* Progress, Apama, and DataDirect * QlikTech and QlikView* Rainstor
* Revolution Analytics* Rocana
* salesforce.com
* SAND Technology
* SAP AG
* SAS Institute
* ScaleBase
* ScaleDB
* Schooner Information Technology* SciDB
* SenSage
* SequoiaDB
* SnapLogic
* Software AG
* solidDB
* Splunk
* Starcounter
* StreamBase
* Sybase
* Syncsort
* Tableau Software
* Talend
* Teradata
* Tokutek and TokuDB* Truviso
* VectorWise
* Vertica Systems
* VoltDB and H-Store* WibiData
* Workday
* Xkoto
* XtremeData
* Yarcdata and Cray
* Zettaset
* Zoomdata
* Data integration and middleware * Application servers * EAI, EII, ETL, ELT, ETLT* Data types
* GIS and geospatial* Object
* RDF and graphs
* Structured documents* Text
* DBMS product categories * Archiving and information preservation * Data warehouse appliances* Mid-range
* NewSQL
* OLTP
* Open source
* Emulation, transparency, portability* Fun stuff
* Humor
* Market share and customer counts * Memory-centric data management* Cache
* In-memory DBMS
* Streaming and complex event processing (CEP) * Michael Stonebraker* Parallelization
* Clustering
* MapReduce
* Transparent sharding* Presentations
* Pricing
* Public policy
* Surveillance and privacy * Software as a Service (SaaS)* Cloud computing
* Specific users
* eBay
* Fox and MySpace
* JPMorgan Chase
* TEOCO
* Yahoo
* Zynga
* Storage
* Solid-state memory * Theory and architecture * Columnar database management * Data models and architecture* Data pipelining
* Database compression * Database diversity* Derived data
* NoSQL
* Petabyte-scale data management* Schema on need
* SQL/Hadoop integration * Workload management* TransRelational
* Uncategorized
*
DATE ARCHIVES
Select Month June 2019 June 2018 May 2018 February 2018 January 2018 December 2017 August 2017 June 2017 April 2017 March 2017 February 2017 December 2016 November 2016 October 2016 September 2016 August 2016 July 2016 June 2016 May 2016 February 2016 January 2016 December 2015 November 2015 October 2015 September 2015 August 2015 July 2015 June 2015 May 2015 April 2015 March 2015 February 2015 January 2015 December 2014 November 2014 October 2014 September 2014 August 2014 July 2014 June 2014 May 2014 April 2014 March 2014 February 2014 January 2014 December 2013 November 2013 October 2013 September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 October 2012 September 2012 August 2012 July 2012 June 2012 May 2012 April 2012 March 2012 February 2012 January 2012 November 2011 October 2011 September 2011 August 2011 July 2011 June 2011 May 2011 April 2011 March 2011 February 2011 January 2011 December 2010 November 2010 October 2010 September 2010 August 2010 July 2010 June 2010 May 2010 April 2010 March 2010 February 2010 January 2010 December 2009 November 2009 October 2009 September 2009 August 2009 July 2009 June 2009 May 2009 April 2009 March 2009 February 2009 January 2009 December 2008 November 2008 October 2008 September 2008 August 2008 July 2008 June 2008 May 2008 April 2008 March 2008 February 2008 January 2008 December 2007 November 2007 October 2007 September 2007 August 2007 July 2007 June 2007 May 2007 April 2007 March 2007 February 2007 January 2007 December 2006 November 2006 October 2006 September 2006 August 2006 July 2006 June 2006 May 2006 April 2006 March 2006 February 2006 January 2006 December 2005 November 2005 October 2005 September 2005 August 2005*
Links
* Monash Research
* White Papers
*
ADMIN
* Log in
* Home
* About
* Contact
* Feeds
Copyright © Monash Research , 2005-2008. Theme designed by Melissa Bradshaw .Details
Copyright © 2024 ArchiveBay.com. All rights reserved. Terms of Use | Privacy Policy | DMCA | 2021 | Feedback | Advertising | RSS 2.0