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Search-Enable Your Application with Lucene
Search-Enable Your Application with Lucene

The e-commerce Web site that I work on has seen several incarnations of its search feature. We started with plain vanilla SQL using "like" clauses, but this didn't perform well and left a lot to be desired in language features such as stemming (e.g., "paint" = "painter" = "painting") and synonym matching (e.g., "cat" = "feline"). Next we tried an off-the-shelf solution. This addressed our efficiency and language demands, but it was ridden with strange quirks and we were limited in how much we could customize its behavior.

Then we discovered Lucene. Lucene is an open-source search framework from Apache's Jakarta project. As a framework, Lucene provides you with the building blocks you need to build a search engine that meets your specific searching requirements. Lucene is flexible, fully customizable, and amazingly fast.

In this article I show you how to use Lucene to build a search solution for your application. Although my examples will be geared toward an e-commerce application, Lucene is flexible enough to be used on any application whether it's Web, desktop, or CD-ROM based.

I used version 1.2 of Lucene to develop the examples in this article. It can be downloaded from http://jakarta.apache.org/lucene. Lucene is self-contained, so you'll need only a JVM (v1.1.8 or higher) to use it. Place lucene-1.2.jar into your classpath and you're ready to start.

Indexing Documents
To build a Lucene index, first you'll need an instance of IndexWriter. The following lines of code create an IndexWriter for an index located at c:\myindex.

Analyzer analyzer = new StopAnalyzer();
writer = new IndexWriter("c:/myindex", analyzer, true);

The first argument to the constructor is the path where the index will be written. If the path doesn't already exist, Lucene will create it for you. The second argument is the Analyzer you want IndexWriter to use when tokenizing text. Here I used StopAnalyzer to remove stop words ("and," "or," "the," etc.) from the token stream. The last argument tells IndexWriter whether to create a new index or to add documents to an existing one. Passing true to the constructor will create the index from scratch; passing false will append to an existing index.

Now that you have an IndexWriter, you're ready to start adding documents to the index. The following code creates a simple document that represents a Web page and uses IndexWriter to add it to the index.

String url = "http://jakarta.apache.org/lucene";
String content = indexer.retrieveWebPageContent(url);
String keywords = indexer.extractKeywords(content);

Document doc = new Document();
doc.add(Field.UnIndexed("url", url));
doc.add(Field.UnStored("keywords", keywords));
doc.add(Field.Text("content", content));
writer.addDocument(doc);

In this example, the document contains the URL metadata for Lucene's homepage, a keywords field that contains search terms to match against in a search, and a "content" field that contains the full content of the Web page.

Once all documents have been added, all that remains is to close the index.

writer.close();

Although this example adds only a single (hard-coded) document to an index, it serves well as a "Hello World" example of how to create indexes using Lucene. The complete source code for this example is in Listing 1. (Listings 1-10 can be downloaded from www.sys-con.com/java/sourcec.cfm.)

For a more interesting example, suppose you're indexing a product catalog to be searched on an e-commerce Web site. A product is made up of a SKU, a name, a price, and some keywords to be searched on (see Listing 2). ProductIndexer (see Listing 3) is a convenience class used to add products to a Lucene index.

The constructor for ProductIndexer takes a string that's the path where the Lucene index will be built and a Boolean parameter that specifies whether a new index will be created or an existing index appended. ProductIndexer uses StopAnalyzer for tokenizing text.

The addProduct() method creates an instance of Document and translates the attributes of the Product into document fields. As in the simple example earlier, the "keywords" field is created as unstored so it can be searched upon but is unavailable for retrieval. The other fields are created as unindexed because these fields will be retrieved only after a successful search, not searched upon themselves.

The close() method closes the IndexWriter, making it available for searching. Before closing, however, a call is made to the IndexWriter's optimize() method to have Lucene optimize the index. Although it's entirely optional, it's generally a good idea to call optimize() if the indexing is finished for the time being and no further documents will be added to the index for a while.

ProductDBIndexer (see Listing 4) reads products from a "catalog" table in a relational database (see Table 1 for the products that I used) and uses ProductIndexer to add the products to Lucene's index. ProductDBIndexer takes two command-line arguments: the path in which to build the index and an optional "create" flag to indicate that the index should be built from scratch.

Lucene Index Structure
Lucene indexes are file based. If you look in the directory where you created the index, you'll find several files that define the Lucene index. Depending on how large your index is, you'll see several groups of files where each file in a group has the same name but a different extension. Each of these groups is known as a "segment." Although this article won't delve into the details of how Lucene segments work, it may be interesting to note that IndexWriter's optimize() method optimizes Lucene's index by consolidating all segments into a single segment for more efficient searching.

While IndexWriter is writing indexes, a file called "write.lock" is created. This file prevents other instances of IndexWriter from writing to the index concurrently. Calling IndexWriter's close() method removes this file and makes the index available for writing by another IndexWriter.

Lucene keeps track of each segment in the index using a file called "segments". During indexing, it occasionally becomes necessary for Lucene to update the segments file to keep it synchronized with the segments in the index. While this synchronization is going on, Lucene creates a "commit.lock" file to prevent concurrent updates of the segments file. Once the segments file is in sync, the commit.lock file is removed.

What would happen if you were to write to an index while it's being searched on? You may write to the index (either by adding new documents or re-creating the index from scratch) while it's being searched, but doing so may have undesirable effects on the search results. The worst side effect that I've seen is a document appearing out of order in the Hits collection. Depending on how important the ordering is to you, it may be best to create your indexes off-line (i.e., in another directory) and then rename the directory to become the current index.

Searching
Now that you've built an index, it's time to perform search queries against it. ProductSearcher (see Listing 5) shows how to do this.

To search a Lucene index you need an instance of org.apache.lucene.search.Searcher. Two subclasses of Searcher come with Lucene. IndexSearcher is for searching a single Lucene index while MultiSearcher is used to search multiple indexes at once. Only the product catalog index will be searched, so IndexSearcher is the best choice for this example. It's constructed given the path to the index.

Searcher searcher = new IndexSearcher(indexPath);

Next you must construct a Query object. The best way to do this is to use the parse() method of org.apache.lucene.queryParser.QueryParser. Create an instance of QueryParser, passing the name of the default field (the field that's searched upon by default) and an analyzer to the constructor. Then call parse() on the QueryParser instance passing the query string. An instance of org.apache .lucene.search.Query will be returned.

QueryParser queryParser = new QueryParser("keywords", new StopAnalyzer());
Query query = queryParser.parse("cat food");

Note: QueryParser is not thread-safe. A new instance of QueryParser should be created for each thread.

For this example the choice of query string is hard coded as "cat food". This query will result in all documents containing either "cat" or "food", but not necessarily both. It's possible to require that a document's keyword field contain "cat" and "food" when searching. Simply place a plus (+) sign in front of each word so that the search string will be "+cat +food" to require resulting documents to contain both "cat" and "food" in their keyword field. More advanced search options will be discussed later.

Next make a call to the Searcher's search() method, passing in the Query object.

Hits hits = searcher.search(query);

The search() method returns an instance of org.apache.lucene.search.Hits. The Hits class represents a collection of documents matching the search criteria, along with each document's relevancy score. These scores range from 0.0 to 1.0 where 1.0 is considered highly relevant and 0.0 is considered completely irrelevant (and not included in the Hits collection).

Finally, cycle through each Document returned in the Hits object displaying the SKU and name of the product along with its relevancy score.

for (int i = 0; i < hits.length(); i++) {
Document document = hits.doc(i);
float score = hits.score(i);
System.out.println(document.get("sku") + " :: " +
document.get("name") + " :: " + score);
}

Advanced Queries
Up until now, the queries have been relatively simple ones such as "cat food" and "+cat +food". QueryParser has a powerful selection of query operators to facilitate more complex searches. Table 2 lists all of QueryParser's operators.

Wildcard queries are fairly straightforward. The "*" operator can be replaced by zero or more characters to match a word. The "?" operator is replaced by exactly one character when matching. For example, "ca*" will match "cat", "car", "cap", or "candle", while "ca?" will match "cat", "car", and "cap", but not "candle". This is consistent with the behavior of "*" and "?" on a DOS or Unix command line.

The tilde (~) character, when used alone, performs a fuzzy search, matching words that are spelled similarly. For example, "cat~" will match "cat", but it will also match "car" and "rat" because these words are similarly spelled.

Surrounding two or more words with quotes (" ") produces a phrase. When two or more words are part of a phrase, those words must appear together in order to be considered a match. For example, ""dog food"" will match documents where "dog" is immediately followed by "food".

If a tilde and a number follow a phrase, then a proximity search is performed. For example, ""dog food"~10" will produce results where "dog" and "food" are found within 10 words of each other, but not necessarily adjacent to each other.

The carat (^) is a term booster. What this means is that any word followed by a carat is considered to have higher relevance than words not followed by a carat. For example, "dog^ kennel" will match where the document contains "dog" or "kennel", but will give a higher relevance to documents containing "dog".

The Boolean operators, AND, OR, and NOT behave as you would expect them to. For example, "(cat AND food) OR bird" returns all documents containing "cat" and "food" along with all documents that contain "bird". "cat NOT food" returns all documents containing "cat", but not containing "food". As you have seen before in the simple "cat food" example, OR is the default conjunction operator.

As shown in the previous example, parentheses can be used to group terms into subqueries.

As discussed, the plus sign (+) requires that a word or phrase exist in a field. Conversely, the minus sign (-) prohibits a word from appearing in the results and is roughly equivalent to NOT. For example, "dog -food" returns all documents containing "dog" but not containing "food".

Finally, there are times when you may want to search multiple fields. When constructing a QueryParser, you must specify a default field to be searched upon. Unless you specify otherwise, any words in your query will be looked for in the default field. In the examples, "keywords" is the default field. You can search on nondefault fields (assuming that they're indexed) by using a colon (:). For example, had the name field been tokenized and indexed, the query string "+cat +name:nummies" would return all documents in which the keywords field contains "cat" and the name field contains "nummies".

Customizing Lucene
While Lucene comes with an impressive set of functionality, you may still find that you want it to do something more or different than is available out of the box. As a search framework, Lucene provides several hooks for you to extend and/or modify its behavior.

In the previous examples, the analyzer chosen was StopAnalyzer. Underneath the covers, Stop-Analyzer uses LetterTokenizer to tokenize text into individual words. LetterTokenizer treats any nonalphabetic character as a delimiter. This is fine in most cases, but what if you want to tokenize text that contains numeric characters ("0" - "9") as well as alphabetic characters? This would be desirable if the keyword text contains part numbers or model numbers. LetterTokenizer wouldn't help in this case.

Listing 6 defines AlphanumericTokenizer, a tokenizer that works like LetterTokenizer except for one small difference: it treats numeric characters as token characters along with alphabetic characters. It does this by subclassing LetterTokenizer and overriding the isTokenChar() method to return the results of LetterTokenizer's isTokenChar() implementation OR'd with a call to Character.isDigit().

AlphanumStopAnalyzer (see Listing 7) is an analyzer that uses AlphanumericTokenizer. The stop-word behavior of StopAnalyzer is still desired, so AlphanumericTokenizer is wrapped with a StopFilter. To normalize the text to lowercase, StopFilter is then wrapped with LowercaseFilter. AlphanumStopAnalyzer is functionally equivalent to StopAnalyzer, except, since it uses AlphanumericTokenizer, it does not treat numeric characters as delimiters. To try out AlphanumStopAnalyzer, use it in place of StopAnalyzer in both ProductIndexer and ProductSearcher. Be sure to reindex with ProductIndexer before searching the index with the new analyzer.

Suppose that synonym-matching capability is required so that "cat" will match "kitten", "kitty", or "feline". AliasFilter (see Listing 8) is a subclass of TokenFilter that does this. AliasFilter retrieves its synonym list from entries in AliasFilter.properties. For example:

cat=feline kitten kitty
dog=canine puppy mutt
food=feed chow
parrot=bird

With each invocation of next(), AliasFilter first checks to see if there are any synonyms in the alias stack. If there are, it pops the next alias off the stack and returns it. Otherwise, AliasFilter retrieves the next token from the input TokenStream, adds any aliases that may exist to the alias stack, and then returns the next token.

AliasAnalyzer (see Listing 9) constructs a TokenStream that does everything the TokenStream from Alphanum-StopAnalyzer does, but it also uses AliasFilter to add synonyms to the TokenStream. To try AliasAnalyzer, use it as your analyzer instead of StopAnalyzer in both ProductIndexer and ProductSearch. Again, be sure to reindex before searching.

When trying AliasFilter you may discover some strange, albeit desirable, behavior. Search for "feline". Even though there are no aliases for feline, all cat-related products appear in the search results. Why? When you use AliasAnalyzer to search for "feline", the token stream does not expand beyond "feline". So why do "cat" products appear? The reason is, you also used AliasAnalyzer to index the products. When you indexed a product containing "cat", AliasAnalyzer expanded the token stream to include "kitten", "kitty", and "feline" in the index. When searching for "feline" it will be found in products whose token stream was expanded to include "feline". In effect, you get an automatic two-way aliasing between "cat" and "feline", even though it appears to be only one way in AliasFilter.properties.

Another common problem in searching is paging the results. A search query could return anywhere from zero results to a seemingly infinite number of result documents. Good usability practices suggest that you page the results, showing the user only a handful at a time. This can be accomplished in Lucene using result filters.

To create a result filter, you must subclass org.apache.lucene.search.Filter. The only required method is the bits() method. It will return a java.util.BitSet where each bit represents a document in the result set. If the bit is true, the document will be returned in Hits, otherwise it won't be returned.

PageFilter (see Listing 10) is an example of a Filter that's used to paginate search results. Given a page number and a page size, PageFilter will pare down Lucene's result set to a specific page's subset of documents. It does this by creating a BitSet big enough to hold the maximum number of result bits and then looping through the bits that need to be turned on. To use PageFilter, change ProductSearcher's call to search() to look like this:

Hits hits = searcher.search(query,new PageFilter(1,20));

This new call to search() will result in showing only the second set of 20 results.

Conclusion
Building a full-featured search engine can be a daunting task. But, thanks to Lucene, much of the complicated details are abstracted behind an easy-to-use API. We've seen how easy it can be to create an index for searching practically any type of information. We've also seen how Lucene is flexible and can be extended to satisfy custom indexing and searching requirements.

Resources

  • Jakarta Lucene: http://jakarta.apache.org/lucene
  • NLucene, the .NET implementation of Lucene at SourceForge: http://sourceforge.net/projects/nlucene
  • JGuru FAQ on Lucene: www.jguru.com/faq/Lucene
  • About Lucene's creator, Doug Cutting: http://lucene.sourceforge.net/background.html

    SIDEBAR
    Index Components
    A Lucene index is a collection of documents organized in a way that allows quick retrieval of information when arbitrarily queried upon.

    Each document (implemented by org.apache.lucene.document.Document) in a Lucene index is made up of one or more fields that are name-value pairs, much like entries in a HashMap. A document can contain as much or as little information as is required to be searched upon. For example, a Lucene document could contain the complete contents of a Web page, text file, e-mail, etc. On the other hand, a Lucene document may contain only a minimal set of metadata, such as keywords, along with a URL, a product SKU, or some other identifying information used to reference a full information source stored outside of Lucene (such as in a file system or a relational database).

    Each field in a document can be defined as being any combination of stored, indexed, and tokenized. If a field is stored, its contents are fully retrievable upon a successful search. If a field is indexed, its content may be referenced in a query and searched upon. If a field is tokenized, its content is broken into one or more tokens (or words) prior to being indexed.

    Fields can be created using org.apache.lucene.document.Field. The Field class has several static factory methods that make short work of creating field entries. Table 3 illustrates these static methods and the types of Fields that they create.

    Why would you want to index a field, but not store it? Consider a field that contains keywords for your document: chances are you'll never display or perform any processing of this field, but you still want to be able to search upon it. By indexing it you're making the field searchable, but by not storing it, you're saving space because the text is not written verbatim to the index. On the other hand, you may want to store some data so that it can be retrieved later but not actually be able to search upon it. In that case, you'd choose a field that's stored but not indexed. When defining your fields, be mindful of what those fields will be used for, and for efficiency's sake choose an appropriate field definition.

    SIDEBAR
    Search Components
    A Searcher (org.apache.lucene.search.Searcher) is used to access a Lucene index and query its contents. There are two subclasses of Searcher: IndexSearcher that searches a single index and MultiSearcher that searches one or more indexes and collects all the results in a single result set.

    Searches are performed by calling one of Searcher's search() methods and passing it a query (org.apache.lucene.search.Query). The search method returns an instance of org.apache.lucene.search.Hits. The Hits class is an array-like collection of documents that matches your query. The documents are ordered in Hits by a relevancy score.

    A Query object can be constructed using org.apache.lucene.query-Parser.QueryParser. QueryParser's parse() method parses a query string that's written in its query language and builds an appropriate Query object for that query string. QueryParser also uses an Analyzer in performing the parsing of the query string. It's not required, but it is strongly recommended that you use the same Analyzer for parsing queries that you used when indexing your documents.

    SIDEBAR
    Text Analysis Components
    When a field is tokenized, its content is broken into one or more tokens or words. Facilitating this tokenization process is the notion of an analyzer (see Figure 1). An analyzer is any subclass of org.apache.lucene.analysis.Analyzer that defines the rules for tokenization.

    A token stream is an iterator that returns the next token with each call to its next() method or returns a null when there are no more tokens in the stream. Two important subclasses of TokenStream are Tokenizer and TokenFilter. Both of these classes are abstract and must be subclassed to define the specific rules on how to tokenize content.

    At the core of the tokenization process is a Tokenizer. A Tokenizer wraps an instance of java.io.Reader and performs the actual work of breaking a stream into individual tokens (not unlike the notion of a StringTokenizer).

    TokenFilters act as decorators of other TokenStreams. Token filters can be used to add, replace, or remove tokens from a TokenStream. For example, org.apache.lucene.analysis.PorterStemFilter is a TokenFilter that replaces each word in a TokenStream with its word stem (e.g., "painting" becomes "paint").

    Analyzers rely on token streams (subclasses of org.apache.lucene.analysis.TokenStream) in defining the tokenization rules. In fact, an analyzer is nothing more than a factory for creating instances of TokenStream.

    To see how the text analysis components are used together, consider some of the TokenStream and Analyzer implementations packaged with Lucene. StopAnalyzer is an analyzer whose job is to remove stop words (e.g., "and", "or", "the", etc.) from a tokenized stream. At the core of StopAnalyzer is an instance of LowerCaseTokenizer. It tokenizes the stream into individual words, normalizing them to lowercase as it goes, where any nonalphabetic character is considered a delimiter. An instance of StopFilter decorates LowerCaseTokenizer, removing stop words from the stream as they're found. StopAnalyzer's tokenStream() method is merely a factory method that returns the decorator chain made up of LowerCaseTokenizer and StopFilter.

    About Craig Walls
    Craig Walls is the manager of Internet development for a Dallas, Texas-based retailer. He has eight years of experience in software development, six in Java. Craig is a Sun Certified Java programmer and a Sun Certified architect for the Java platform. He holds a BS in computer science from New Mexico State University.

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    In a recent survey, Sumo Logic surveyed 1,500 customers who employ cloud services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). According to the survey, a quarter of the respondents have already deployed Docker containers and nearly as many (23 percent) are employing the AWS Lambda serverless computing framework. It’s clear: serverless is here to stay. The adoption does come with some needed changes, within both application development and operations. Tha...
    Widespread fragmentation is stalling the growth of the IIoT and making it difficult for partners to work together. The number of software platforms, apps, hardware and connectivity standards is creating paralysis among businesses that are afraid of being locked into a solution. EdgeX Foundry is unifying the community around a common IoT edge framework and an ecosystem of interoperable components.
    In this strange new world where more and more power is drawn from business technology, companies are effectively straddling two paths on the road to innovation and transformation into digital enterprises. The first path is the heritage trail – with “legacy” technology forming the background. Here, extant technologies are transformed by core IT teams to provide more API-driven approaches. Legacy systems can restrict companies that are transitioning into digital enterprises. To truly become a lead...
    Data scientists must access high-performance computing resources across a wide-area network. To achieve cloud-based HPC visualization, researchers must transfer datasets and visualization results efficiently. HPC clusters now compute GPU-accelerated visualization in the cloud cluster. To efficiently display results remotely, a high-performance, low-latency protocol transfers the display from the cluster to a remote desktop. Further, tools to easily mount remote datasets and efficiently transfer...
    "Cloud Academy is an enterprise training platform for the cloud, specifically public clouds. We offer guided learning experiences on AWS, Azure, Google Cloud and all the surrounding methodologies and technologies that you need to know and your teams need to know in order to leverage the full benefits of the cloud," explained Alex Brower, VP of Marketing at Cloud Academy, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clar...
    Enterprises are adopting Kubernetes to accelerate the development and the delivery of cloud-native applications. However, sharing a Kubernetes cluster between members of the same team can be challenging. And, sharing clusters across multiple teams is even harder. Kubernetes offers several constructs to help implement segmentation and isolation. However, these primitives can be complex to understand and apply. As a result, it’s becoming common for enterprises to end up with several clusters. Thi...
    DevOps promotes continuous improvement through a culture of collaboration. But in real terms, how do you: Integrate activities across diverse teams and services? Make objective decisions with system-wide visibility? Use feedback loops to enable learning and improvement? With technology insights and real-world examples, in his general session at @DevOpsSummit, at 21st Cloud Expo, Andi Mann, Chief Technology Advocate at Splunk, explored how leading organizations use data-driven DevOps to close th...
    The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform. In his session at @ThingsExpo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and shared the must-have mindsets for removing complexity from the develop...
    Companies are harnessing data in ways we once associated with science fiction. Analysts have access to a plethora of visualization and reporting tools, but considering the vast amount of data businesses collect and limitations of CPUs, end users are forced to design their structures and systems with limitations. Until now. As the cloud toolkit to analyze data has evolved, GPUs have stepped in to massively parallel SQL, visualization and machine learning.
    The next XaaS is CICDaaS. Why? Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But… securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous ar...
    To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to ...
    Large industrial manufacturing organizations are adopting the agile principles of cloud software companies. The industrial manufacturing development process has not scaled over time. Now that design CAD teams are geographically distributed, centralizing their work is key. With large multi-gigabyte projects, outdated tools have stifled industrial team agility, time-to-market milestones, and impacted P&L stakeholders.
    In his session at 21st Cloud Expo, James Henry, Co-CEO/CTO of Calgary Scientific Inc., introduced you to the challenges, solutions and benefits of training AI systems to solve visual problems with an emphasis on improving AIs with continuous training in the field. He explored applications in several industries and discussed technologies that allow the deployment of advanced visualization solutions to the cloud.

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    Cloud Expo Silicon Valley All-Star Speakers Include

    RICHARDS
    Venafi

    SINGH
    IBM

    NIELSON
    Redis Labs

    FRIEDMAN
    Edge

    THIELE
    Apcera

    MONTE-
    CILLO

    IBM

    SPROULE
    Metavine

    REUVENI
    Jet

    CHITTURI
    Sungard

    CEPPI
    Canonical

    HAFF
    Red Hat

    CHAMBLISS
    ReadyTalk

    BLACK
    SQLstream

    KOCHER
    Grey Heron

    VISWA-
    NATHAN

    Cognizant

    LARSSON
    Qosmos

    COHEN
    Institute

    SCHEELE
    Loodse

    FEATHER-
    STON

    Collaborative
    Consulting

    SEHGAL
    Tintri

    SRINIVAS
    IBM

    AHUJA
    Impiger

    SUDH-
    AKAR

    Splunk

    TISH-
    GART

    Cloudera

    KENDRICK
    Isomorphic

    KUCKEIN
    DDN

    RAVE
    Teridion

    CHOU
    Microsoft

    SHARIF
    Aporeto

    JAME-
    NSKY

    Embotics

    JENKINS
    IBM

    DOYLE
    eCube

    THOM-
    CHICK

    Symantec

    BRENTON
    CollabNet

    BERCO-
    VICI

    SolidFire

    BABIN
    Kaltura

    MORAN
    BMC

    CASEY
    CFN

    ZHENG
    CDS

    DEMEO
    Alfresco

    STIGTER
    TIBCO

    LEUFFEN
    Wrecker

    ROGERS
    Anexia

    NEW
    HOUSE

    Agilitiv

    DE MENO
    Commvault

    VAN TUIN
    Red Hat

    LIANG
    Rancher Labs

    MURTHY
    Cloud Raxak

    REMIL-
    LARD

    Microsoft

    MANN
    Splunk

    ROWE
    IBM Cloud

    SKILLERN
    Intel

    ALLEN
    Progress

    BENEDICT
    Cognizant
    Cloud Expo New York All-Star Speakers Include

    DE MENO
    Commvault

    ERWAY
    Appneta

    OXENHORN
    FalconStor

    HINCH-
    CLIFFE

    7Summits

    DOYLE
    eCube

    CHAVES
    Pythian

    BLOOMBERG
    Intellyx

    BUGAYENKO
    Teamed.io

    LAWSON
    NewSci

    HAFF
    RedHat

    HOLT
    IBM

    Jewell
    Codenvy

    MORGENTHAL
    CSC

    ARMSTRONG
    AppNeta

    DWYER
    Iron.io

    MATSUMURA
    Gradle

    WARFIELD
    Coho

    GRECO
    Kaazing

    PRESLEY
    Pythian

    LOVELL-TROY
    Pythian

    ATCHISON
    New Relic

    BHUSVANE-
    SHWARI

    Microsoft

    GUCCIONE
    Keeper

    FLOREA
    Tintri

    KERBY
    BMC

    RAO
    Asurion

    BRENTON
    CollabNet

    SRINIVASAN
    Symantec

    GALBRAITH
    HPE

    NEWTON
    Alfresco

    ROGERS
    Anexia

    MORRISH
    Interoute

    TIFFANY
    SoftLayer

    OSTROVER-
    KHYI

    Mobidev

    LEWIS
    Formation
    Data

    KENDRICK
    Isomorphic

    REEVES
    Datical

    WALLER-
    STORFER

    Dynatrace

    CIOT
    Progress

    MOR
    Cloudyn

    LEFORT
    BMC

    ANDERSON
    BMC

    BRODY
    Webair

    NIELSEN
    Redis Labs

    SEHGAL
    Tintri

    MANN
    Splunk

    YOO
    Fuze

    COTY
    Alert Logic

    LANDA
    Kintone

    CHOQUETTE
    RackN

    MARTIN
    Security

    HURWITZ
    Hurwitz & Assc.

    Cloud Expo Silicon Valley All-Star Speakers Include

    KOWALL
    AppDynamics

    VAN TUIN
    Red Hat

    DEMMER
    Jut

    COHEN
    Netflix

    MUCHANDI
    Red Hat Inc

    BORELLO
    Sysdig

    GILPIN
    Conjur

    KANADE
    Harbinger
    Systems

    GORBACHEV
    Systems
    Services Inc.

    SUSSMAN
    Coalfire

    KHAN
    Solgeniakhela

    CHOKSI
    Harbinger
    Systems

    DE MENO
    CommVault

    BLOOMBERG
    Intellyx

    BUGWADIA
    Nirmata

    COTY
    Alert Logic

    FLETCHER
    Alert Logic

    CAUTHRON
    NIMBOXX

    LYNN
    AgilData

    WAGNER
    Cloudyn

    ANAND
    Appocito

    WEISS
    Pythian

    BRODY
    Webair

    JACKSON
    Softlayer

    DAVIDSON
    Juniper

    HOFFMAN
    Pivotal

    VERVAET
    HGST

    Murthy
    CloudRaxak

    FRANCISCO
    Evolute

    LETCHIN
    Nexenta

    SIMON
    JFrog

    BONIFAZI
    Solgenia

    WEISS
    ProfitBricks

    HOLT
    IBM

    HANNON
    SoftLayer

    GALLO
    SoftLayer

    SAYEGH
    Codero

    BIMMU
    Clouber

    SRINIVAS
    IBM

    CHAVAN
    IBM

    HEDGES
    Clouddata

    AHUJA
    Cloud

    MEINER
    Oracle

    SWARTZ
    Ericsson
    Cloud Expo New York All-Star Speakers Included

    DE SOUZA
    Cisco

    POTTER
    SafeLogic

    ROBINSON
    CompTIA

    WARUSA
    -WITHANA

    WSO2 Inc

    MEINER
    Oracle

    CHOU
    Microsoft

    HARRISON
    Tufin

    BRUNOZZI
    VMware

    KIM
    MapR

    KANE
    Dyn

    SICULAR
    Basho

    TURNER
    Cloudian

    KUMAR
    Liaison

    ADAMIAK
    Liaison

    KHAN
    Solgenia

    BONIFAZI
    Solgenia

    SUSSMAN
    Coalfire

    ISAACSON
    RMS

    LYNN
    CodeFutures

    HEABERLIN
    Windstream

    RAMA
    MURTHY

    Virtusa

    BOSTOCK
    IndependenceIT

    DE MENO
    CommVault

    GRILLI
    Adobe

    WILLIAMS
    Rancher Labs

    CRISWELL
    Alert Logic

    COTY
    Alert Logic

    JACOBS
    SingleHop

    MARAVEI
    Cisco

    JACKSON
    Softlayer

    SINGH
    IBM

    HAZARD
    Softlayer

    GALLO
    Softlayer

    TAMASKAR
    GENBAND

    SUBRA
    -MANIAN

    Emcien

    LEVESQUE
    Windstream

    IVANOV
    StorPool

    BLOOMBERG
    Intellyx

    BUDHANI
    Soha

    HATHAWAY
    IBM Watson

    TOLL
    ProfitBricks

    LANDRY
    Microsoft

    BEARFIELD
    Blue Box

    HERITAGE
    Akana

    PILUSO
    SIASMSP

    HOLT
    IBM Cloudant

    SHAN
    CTS

    PICCININNI
    EMC

    BRON-
    GERSMA

    Modulus

    PAIGE
    CenturyLink

    SABHIKHI
    Cognitive Scale

    MILLS
    Green House Data

    KATZEN
    CenturyLink

    SLOPER
    CenturyLink

    SRINIVAS
    EMC

    TALREJA
    Cisco

    GORBACHEV
    Systems Services Inc.

    COLLISON
    Apcera

    PRABHU
    OpenCrowd

    LYNN
    CodeFutures

    SWARTZ
    Ericsson

    MOSHENKO
    CoreOS

    BERMINGHAM
    SIOS

    WILLIS
    Stateless Networks

    MURPHY
    Gridstore

    KHABE
    Vicom

    NIKOLOV
    GetClouder

    DIETZE
    Windstream

    DALRYMPLE
    EnterpriseDB

    MAZZUCCO
    TierPoint

    RIVERA
    WHOA.com

    HERITAGE
    Akana

    SEYMOUR
    6fusion

    GIANNETTO
    Author

    CARTER
    IBM

    ROGERS
    Virtustream
    Cloud Expo Silicon Valley All-Star Speakers

    TESAR
    Microsoft

    MICKOS
    HP

    BHARGAVA
    Intel

    RILEY
    Riverbed

    DEVINE
    IBM

    ISAACSON
    CodeFutures

    LYNN
    HP

    HINKLE
    Citrix

    KHAN
    Solgenia

    SINGH
    Bigdata

    BEACH
    SendGrid

    BOSTOCK
    IndependenceIT

    DE SOUZA
    Cisco

    PATTATHIL
    Harbinger

    O'BRIEN
    Aria Systems

    BONIFAZI
    Solgenia

    BIANCO
    Solgenia

    PROCTOR
    NuoDB

    DUGGAL
    EnterpriseWeb

    TEGETHOFF
    Appcore

    BRUNOZZI
    VMware

    HICKENS
    Parasoft

    KLEBANOV
    Cisco

    PETERS
    Esri

    GOLDBERG
    Vormetric

    CUMBER-
    LAND

    Dimension

    ROSENDAHL
    Quantum

    LOOMIS
    Cloudant

    BRUNO
    StackIQ

    HANNON
    SoftLayer

    JACKSON
    SoftLayer

    HOCH
    Virtustream

    KAPADIA
    Seagate

    PAQUIN
    OnLive

    TSAI
    Innodisk

    BARRALL
    Connected Data

    SHIAH
    AgilePoint

    SEGIL
    Verizon

    PODURI
    Citrix

    COWIE
    Dyn

    RITTEN-
    HOUSE

    Cisco

    FALLOWS
    Kaazing

    THYKATTIL
    TimeWarner

    LEIDUCK
    SAP

    LYNN
    HP

    WAGSTAFF
    BSQUARE

    POLLACK
    AOL

    KAMARAJU
    Vormetric

    BARRY
    Catbird

    MENDEN-
    HALL

    SUPERNAP

    SHAN
    KEANE

    PLESE
    Verizon

    BARNUM
    Voxox

    TURNER
    Cloudian

    CALDERON
    Advanced Systems

    AGARWAL
    SOA Software

    LEE
    Quantum

    OBEROI
    Concurrent, Inc.

    HATEM
    Verizon

    GALEY
    Autodesk

    CAUTHRON
    NIMBOXX

    BARSOUM
    IBM

    GORDON
    1Plug

    LEWIS
    Verizon

    YEO
    OrionVM

    NAKAGAWA
    Transparent Cloud Computing

    SHIBATA
    Transparent Cloud Computing

    NATH
    GE

    GOKCEN
    GE

    STOICA
    Databricks

    TANKEL
    Pivotal Software



    Testimonials
    This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
    @SteveMar_Msft
    General Manager of Window Azure
     
    How does Cloud Expo do it every year? Another INCREDIBLE show - our heads are spinning - so fun and informative."
    @SOASoftwareInc
     
    Thank you @ThingsExpo for such a great event. All of the people we met over the past three days makes us confident IoT has a bright future."
    Yasser Khan
    CEO of @Cnnct2me
     
    One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

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    @CloudExpo Blogs
    "Storpool does only block-level storage so we do one thing extremely well. The growth in data is what drives the move to software-defined technologies in general and software-defined storage," explained Boyan Ivanov, CEO and co-founder at StorPool, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
    As Marc Andreessen says software is eating the world. Everything is rapidly moving toward being software-defined – from our phones and cars through our washing machines to the datacenter. However, there are larger challenges when implementing software defined on a larger scale - when building software defined infrastructure. In his session at 16th Cloud Expo, Boyan Ivanov, CEO of StorPool, provided some practical insights on what, how and why when implementing "software-defined" in the datacenter.
    Blockchain. A day doesn’t seem to go by without seeing articles and discussions about the technology. According to PwC executive Seamus Cushley, approximately $1.4B has been invested in blockchain just last year. In Gartner’s recent hype cycle for emerging technologies, blockchain is approaching the peak. It is considered by Gartner as one of the ‘Key platform-enabling technologies to track.’ While there is a lot of ‘hype vs reality’ discussions going on, there is no arguing that blockchain is being taken very seriously across industries and cannot be ignored.
    The nature of test environments is inherently temporary—you set up an environment, run through an automated test suite, and then tear down the environment. If you can reduce the cycle time for this process down to hours or minutes, then you may be able to cut your test environment budgets considerably. The impact of cloud adoption on test environments is a valuable advancement in both cost savings and agility. The on-demand model takes advantage of public cloud APIs requiring only payment for the time needed to run automated tests. In this framework, success depends on two things: automated i...
    Blockchain is a shared, secure record of exchange that establishes trust, accountability and transparency across business networks. Supported by the Linux Foundation's open source, open-standards based Hyperledger Project, Blockchain has the potential to improve regulatory compliance, reduce cost as well as advance trade. Are you curious about how Blockchain is built for business? In her session at 21st Cloud Expo, René Bostic, Technical VP of the IBM Cloud Unit in North America, discussed the basics of Blockchain, previewed the Blockchain Reference Architecture, and introduced the mechanics o...
    ChatOps is an emerging topic that has led to the wide availability of integrations between group chat and various other tools/platforms. Currently, HipChat is an extremely powerful collaboration platform due to the various ChatOps integrations that are available. However, DevOps automation can involve orchestration and complex workflows. In his session at @DevOpsSummit at 20th Cloud Expo, Himanshu Chhetri, CTO at Addteq, will cover practical examples and use cases such as self-provisioning infrastructure/applications, self-remediation workflows, integrating monitoring and complimenting integra...
    BnkToTheFuture.com is the largest online investment platform for investing in FinTech, Bitcoin and Blockchain companies. We believe the future of finance looks very different from the past and we aim to invest and provide trading opportunities for qualifying investors that want to build a portfolio in the sector in compliance with international financial regulations.
    You know you need the cloud, but you’re hesitant to simply dump everything at Amazon since you know that not all workloads are suitable for cloud. You know that you want the kind of ease of use and scalability that you get with public cloud, but your applications are architected in a way that makes the public cloud a non-starter. You’re looking at private cloud solutions based on hyperconverged infrastructure, but you’re concerned with the limits inherent in those technologies.
    A strange thing is happening along the way to the Internet of Things, namely far too many devices to work with and manage. It has become clear that we'll need much higher efficiency user experiences that can allow us to more easily and scalably work with the thousands of devices that will soon be in each of our lives. Enter the conversational interface revolution, combining bots we can literally talk with, gesture to, and even direct with our thoughts, with embedded artificial intelligence, which can process our conversational commands and orchestrate the outcomes we request across our persona...
    It has never been a better time to be a developer! Thanks to cloud computing, deploying our applications is much easier than it used to be. How we deploy our apps continues to evolve thanks to cloud hosting, Platform-as-a-Service (PaaS), and now Function-as-a-Service. FaaS is the concept of serverless computing via serverless architectures. Software developers can leverage this to deploy an individual "function", action, or piece of business logic. They are expected to start within milliseconds and process individual requests and then the process ends.
    The use of containers by developers -- and now increasingly IT operators -- has grown from infatuation to deep and abiding love. But as with any long-term affair, the honeymoon soon leads to needing to live well together ... and maybe even getting some relationship help along the way. And so it goes with container orchestration and automation solutions, which are rapidly emerging as the means to maintain the bliss between rapid container adoption and broad container use among multiple cloud hosts. This BriefingsDirect cloud services maturity discussion focuses on new ways to gain container orc...
    Imagine if you will, a retail floor so densely packed with sensors that they can pick up the movements of insects scurrying across a store aisle. Or a component of a piece of factory equipment so well-instrumented that its digital twin provides resolution down to the micrometer.
    The need for greater agility and scalability necessitated the digital transformation in the form of following equation: monolithic to microservices to serverless architecture (FaaS). To keep up with the cut-throat competition, the organisations need to update their technology stack to make software development their differentiating factor. Thus microservices architecture emerged as a potential method to provide development teams with greater flexibility and other advantages, such as the ability to deliver applications at warp speed using infrastructure as a service (IaaS) and platform as a ...
    In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management c...
    Product connectivity goes hand and hand these days with increased use of personal data. New IoT devices are becoming more personalized than ever before. In his session at 22nd Cloud Expo | DXWorld Expo, Nicolas Fierro, CEO of MIMIR Blockchain Solutions, will discuss how in order to protect your data and privacy, IoT applications need to embrace Blockchain technology for a new level of product security never before seen - or needed.
    Bitcoins are a digital cryptocurrency and have been around since 2009. As a substitute for legal tender, they are becoming the rage for investors and others but because there is no government agency auditing or performing regulatory oversights, you wonder if it is the perfect breeding ground for electronic nano crime. Since the introduction of the Bitcoin, some competitors have emerged and the whole segment of cryptocurrencies are defined as Altcoins. Altcoins include Dogecoin, Ethereum Feathercoin, Litecoin, Novacoin, Peercoin, and Zetacoin. Some of these cryptocurrencies are considered impro...
    Leading companies, from the Global Fortune 500 to the smallest companies, are adopting hybrid cloud as the path to business advantage. Hybrid cloud depends on cloud services and on-premises infrastructure working in unison. Successful implementations require new levels of data mobility, enabled by an automated and seamless flow across on-premises and cloud resources. In his general session at 21st Cloud Expo, Greg Tevis, an IBM Storage Software Technical Strategist and Customer Solution Architect, explored how storage and software-defined solutions from IBM have evolved for the road ahead. Lea...
    Nordstrom is transforming the way that they do business and the cloud is the key to enabling speed and hyper personalized customer experiences. In his session at 21st Cloud Expo, Ken Schow, VP of Engineering at Nordstrom, discussed some of the key learnings and common pitfalls of large enterprises moving to the cloud. This includes strategies around choosing a cloud provider(s), architecture, and lessons learned. In addition, he covered some of the best practices for structured team migration and discussed ways to control cloud costs.
    In his general session at 21st Cloud Expo, Greg Dumas, Calligo’s Vice President and G.M. of US operations, discussed the new Global Data Protection Regulation and how Calligo can help business stay compliant in digitally globalized world. Greg Dumas is Calligo's Vice President and G.M. of US operations. Calligo is an established service provider that provides an innovative platform for trusted cloud solutions. Calligo’s customers are typically most concerned about GDPR compliance, application performance guarantees & data privacy.
    Blockchain offers impeccable security with its cryptography-based decentralized system as well as the plethora of possible uses retailers could exploit in the near future. In a world of increasing cyberattacks, internet fraud and online hacking, blockchain comes as a breath of fresh air. With its encrypted data and decentralized network system, it's a thorn in every hacker's side. Generally being associated with the finance sector, blockchain is now taking retail by storm. It's on a course that will change the retail industry as we know it. But how exactly is it going to achieve such a feat?