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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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    Advances in technology and ubiquitous connectivity have made the utilization of a dispersed workforce more common. Whether that remote team is located across the street or country, management styles/ approaches will have to be adjusted to accommodate this new dynamic. In his session at 17th Cloud Expo, Sagi Brody, Chief Technology Officer at Webair Internet Development Inc., focused on the challenges of managing remote teams, providing real-world examples that demonstrate what works and what do...
    Most people haven’t heard the word, “gamification,” even though they probably, and perhaps unwittingly, participate in it every day. Gamification is “the process of adding games or game-like elements to something (as a task) so as to encourage participation.” Further, gamification is about bringing game mechanics – rules, constructs, processes, and methods – into the real world in an effort to engage people. In his session at @ThingsExpo, Robert Endo, owner and engagement manager of Intrepid D...
    The modern software development landscape consists of best practices and tools that allow teams to deliver software in a near-continuous manner. By adopting a culture of automation, measurement and sharing, the time to ship code has been greatly reduced, allowing for shorter release cycles and quicker feedback from customers and users. Still, with all of these tools and methods, how can teams stay on top of what is taking place across their infrastructure and codebase? Hopping between services a...
    Learn how IoT, cloud, social networks and last but not least, humans, can be integrated into a seamless integration of cooperative organisms both cybernetic and biological. This has been enabled by recent advances in IoT device capabilities, messaging frameworks, presence and collaboration services, where devices can share information and make independent and human assisted decisions based upon social status from other entities. In his session at @ThingsExpo, Michael Heydt, founder of Seamless...
    Mobile testing is getting harder: more devices, multiple operating systems, higher quality expectations and shorter development cycles. In his session at DevOps Summit, Tom Chavez, Senior Evangelist at SOASTA, discussed the seven steps to improving your mobile testing process. Tom Chavez, with 20+ years of experience as a product manager in software development tools, works in product management at SOASTA, the leader in performance analytics. He has worked across the Silicon Valley at industr...
    The IoT's basic concept of collecting data from as many sources possible to drive better decision making, create process innovation and realize additional revenue has been in use at large enterprises with deep pockets for decades. So what has changed? In his session at @ThingsExpo, Prasanna Sivaramakrishnan, Solutions Architect at Red Hat, discussed the impact commodity hardware, ubiquitous connectivity, and innovations in open source software are having on the connected universe of people, thi...
    Rapid innovation, changing business landscapes, and new IT demands force businesses to make changes quickly. The DevOps approach is a way to increase business agility through collaboration, communication, and integration across different teams in the IT organization. In his session at @DevOpsSummit, Chris Van Tuin, Chief Technologist for the Western US at Red Hat, discussed: The acceleration of application delivery for the business with DevOps
    For it to be SOA – let alone SOA done right – we need to pin down just what "SOA done wrong" might be. First-generation SOA with Web Services and ESBs, perhaps? But then there's second-generation, REST-based SOA. More lightweight and cloud-friendly, but many REST-based SOA practices predate the microservices wave. Today, microservices and containers go hand in hand – only the details of "container-oriented architecture" are largely on the drawing board – and are not likely to look much like S...
    The Microservices architectural pattern promises increased DevOps agility and can help enable continuous delivery of software. This session is for developers who are transforming existing applications to cloud-native applications, or creating new microservices style applications. In his session at 17th Cloud Expo, Jim Bugwadia, CEO of Nirmata, introduced best practices, patterns, challenges, and solutions for the development and operations of microservices style applications. He discussed how a...
    There are so many tools and techniques for data analytics that even for a data scientist the choices, possible systems, and even the types of data can be daunting. In his session at @ThingsExpo, Chris Harrold, Global CTO for Big Data Solutions for EMC Corporation, showed how to perform a simple, but meaningful analysis of social sentiment data using freely available tools that take only minutes to download and install. Participants received the download information, scripts, and complete end-t...
    For manufacturers, the Internet of Things (IoT) represents a jumping-off point for innovation, jobs, and revenue creation. But to adequately seize the opportunity, manufacturers must design devices that are interconnected, can continually sense their environment and process huge amounts of data. As a first step, manufacturers must embrace a new product development ecosystem in order to support these products.
    Clearly the way forward is to move to cloud be it bare metal, VMs or containers. One aspect of the current public clouds that is slowing this cloud migration is cloud lock-in. Every cloud vendor is trying to make it very difficult to move out once a customer has chosen their cloud. In his session at 17th Cloud Expo, Naveen Nimmu, CEO of Clouber, Inc., advocated that making the inter-cloud migration as simple as changing airlines would help the entire industry to quickly adopt the cloud without ...
    Everyone talks about continuous integration and continuous delivery but those are just two ends of the pipeline. In the middle of DevOps is continuous testing (CT), and many organizations are struggling to implement continuous testing effectively. After all, without continuous testing there is no delivery. And Lab-As-A-Service (LaaS) enhances the CT with dynamic on-demand self-serve test topologies. CT together with LAAS make a powerful combination that perfectly serves complex software developm...
    @CloudExpo Stories
    Increasing IoT connectivity is forcing enterprises to find elegant solutions to organize and visualize all incoming data from these connected devices with re-configurable dashboard widgets to effectively allow rapid decision-making for everything from immediate actions in tactical situations to strategic analysis and reporting. In his session at 18th Cloud Expo, Shikhir Singh, Senior Developer Relations Manager at Sencha, will discuss how to create HTML5 dashboards that interact with IoT devic...
    The increasing popularity of the Internet of Things necessitates that our physical and cognitive relationship with wearable technology will change rapidly in the near future. This advent means logging has become a thing of the past. Before, it was on us to track our own data, but now that data is automatically available. What does this mean for mHealth and the "connected" body? In her session at @ThingsExpo, Lisa Calkins, CEO and co-founder of Amadeus Consulting, will discuss the impact of wea...
    trust and privacy in their ecosystem. Assurance and protection of device identity, secure data encryption and authentication are the key security challenges organizations are trying to address when integrating IoT devices. This holds true for IoT applications in a wide range of industries, for example, healthcare, consumer devices, and manufacturing. In his session at @ThingsExpo, Lancen LaChance, vice president of product management, IoT solutions at GlobalSign, will teach IoT developers how t...
    There is an ever-growing explosion of new devices that are connected to the Internet using “cloud” solutions. This rapid growth is creating a massive new demand for efficient access to data. And it’s not just about connecting to that data anymore. This new demand is bringing new issues and challenges and it is important for companies to scale for the coming growth. And with that scaling comes the need for greater security, gathering and data analysis, storage, connectivity and, of course, the...
    The IoTs 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, will demonstrate how to move beyond today's coding paradigm and share the must-have mindsets for removing complexity from the development proc...
    Artificial Intelligence has the potential to massively disrupt IoT. In his session at 18th Cloud Expo, AJ Abdallat, CEO of Beyond AI, will discuss what the five main drivers are in Artificial Intelligence that could shape the future of the Internet of Things. AJ Abdallat is CEO of Beyond AI. He has over 20 years of management experience in the fields of artificial intelligence, sensors, instruments, devices and software for telecommunications, life sciences, environmental monitoring, process...
    SYS-CON Events announced today that Ericsson has been named “Gold Sponsor” of SYS-CON's @ThingsExpo, which will take place on June 7-9, 2016, at the Javits Center in New York, New York. Ericsson is a world leader in the rapidly changing environment of communications technology – providing equipment, software and services to enable transformation through mobility. Some 40 percent of global mobile traffic runs through networks we have supplied. More than 1 billion subscribers around the world re...
    In the world of DevOps there are ‘known good practices’ – aka ‘patterns’ – and ‘known bad practices’ – aka ‘anti-patterns.' Many of these patterns and anti-patterns have been developed from real world experience, especially by the early adopters of DevOps theory; but many are more feasible in theory than in practice, especially for more recent entrants to the DevOps scene. In this power panel at @DevOpsSummit at 18th Cloud Expo, moderated by DevOps Conference Chair Andi Mann, panelists will dis...
    Much of the value of DevOps comes from a (renewed) focus on measurement, sharing, and continuous feedback loops. In increasingly complex DevOps workflows and environments, and especially in larger, regulated, or more crystallized organizations, these core concepts become even more critical. In his session at @DevOpsSummit at 18th Cloud Expo, Andi Mann, Chief Technology Advocate at Splunk, will show how, by focusing on 'metrics that matter,' you can provide objective, transparent, and meaningfu...
    In his session at 18th Cloud Expo, Sagi Brody, Chief Technology Officer at Webair Internet Development Inc., will focus on real world deployments of DDoS mitigation strategies in every layer of the network. He will give an overview of methods to prevent these attacks and best practices on how to provide protection in complex cloud platforms. He will also outline what we have found in our experience managing and running thousands of Linux and Unix managed service platforms and what specifically c...
    Many private cloud projects were built to deliver self-service access to development and test resources. While those clouds delivered faster access to resources, they lacked visibility, control and security needed for production deployments. In their session at 18th Cloud Expo, Steve Anderson, Product Manager at BMC Software, and Rick Lefort, Principal Technical Marketing Consultant at BMC Software, will discuss how a cloud designed for production operations not only helps accelerate developer...
    Redis is not only the fastest database, but it has become the most popular among the new wave of applications running in containers. Redis speeds up just about every data interaction between your users or operational systems. In his session at 18th Cloud Expo, Dave Nielsen, Developer Relations at Redis Labs, will shares the functions and data structures used to solve everyday use cases that are driving Redis' popularity.
    See storage differently! Storage performance problems have only gotten worse and harder to solve as applications have become largely virtualized and moved to a cloud-based infrastructure. Storage performance in a virtualized environment is not just about IOPS, it is about how well that potential performance is guaranteed to individual VMs for these apps as the number of VMs keep going up real time. In his session at 18th Cloud Expo, Dhiraj Sehgal, in product and marketing at Tintri, will discu...
    Whether your IoT service is connecting cars, homes, appliances, wearable, cameras or other devices, one question hangs in the balance – how do you actually make money from this service? The ability to turn your IoT service into profit requires the ability to create a monetization strategy that is flexible, scalable and working for you in real-time. It must be a transparent, smoothly implemented strategy that all stakeholders – from customers to the board – will be able to understand and comprehe...
    You deployed your app with the Bluemix PaaS and it's gaining some serious traction, so it's time to make some tweaks. Did you design your application in a way that it can scale in the cloud? Were you even thinking about the cloud when you built the app? If not, chances are your app is going to break. Check out this webcast to learn various techniques for designing applications that will scale successfully in Bluemix, for the confidence you need to take your apps to the next level and beyond.
    SYS-CON Events announced today that Peak 10, Inc., a national IT infrastructure and cloud services provider, will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. Peak 10 provides reliable, tailored data center and network services, cloud and managed services. Its solutions are designed to scale and adapt to customers’ changing business needs, enabling them to lower costs, improve performance and focus inter...
    So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, will provide tips on how to be successful in large scale machine lear...
    SYS-CON Events announced today that SoftLayer, an IBM Company, has been named “Gold Sponsor” of SYS-CON's 18th Cloud Expo, which will take place on June 7-9, 2016, at the Javits Center in New York, New York. SoftLayer, an IBM Company, provides cloud infrastructure as a service from a growing number of data centers and network points of presence around the world. SoftLayer’s customers range from Web startups to global enterprises.
    SYS-CON Events announced today that Enzu, a leading provider of cloud hosting solutions, will exhibit at SYS-CON's 18th International Cloud Expo®, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. Enzu’s mission is to be the leading provider of enterprise cloud solutions worldwide. Enzu enables online businesses to use its IT infrastructure to their competitive advantage. By offering a suite of proven hosting and management services, Enzu wants companies to foc...
    Internap Corporation has expanded its OpenStack-based bare-metal Infrastructure-as-a-Service offering, AgileSERVER 2.0, to its data centers in Amsterdam, Dallas and Santa Clara, Calif. Launched in 2015 out of Internap’s New York Metro data center in Secaucus, N.J., AgileSERVER 2.0 is now available in four locations globally, enabling enterprises and devops teams running mission-critical applications and big data workloads to build scale-out infrastructure environments that are higher performing ...

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    Cloud Expo New York All-Star Speakers Include

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    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."
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    @CloudExpo Blogs
    The cloud provides two major advantages to load and performance procedures that help testing teams better model realistic behavior: instant infrastructure and geographic location. Cloud-based load testing also lowers the total cost of ownership, increases flexibility and allows testers to understand the impact of third-party components. So you’re sold on using the cloud; here’s what you should look for in a cloud-based load testing solution.
    Developing solutions integrated with mobile applications that can anticipate your needs based upon your Code Halo data, the information that surrounds people, organizations, projects, activities and devices, and acting upon it automatically is where a large amount of productivity gains will be found in the future.
    The benefits of efficiency and lower cost have been the primary drivers toward the cloud and SaaS deployments, but this is just the low-hanging fruit of a much greater body of potential. According to a Cisco-sponsored IDC report, a second wave of cloud adoption now targets a much more strategic set of benefits, with 54 percent of businesses surveyed expecting cloud to allow them to allocate IT budgets more strategically, and 53 percent looking to cloud solutions to increase revenues. According to the Cisco study, cloud and SaaS adoption takes place along a spectrum, with ad hoc deployment on...
    The increasing popularity of the Internet of Things necessitates that our physical and cognitive relationship with wearable technology will change rapidly in the near future. This advent means logging has become a thing of the past. Before, it was on us to track our own data, but now that data is automatically available. What does this mean for mHealth and the "connected" body? In her session at @ThingsExpo, Lisa Calkins, CEO and co-founder of Amadeus Consulting, will discuss the impact of wearables, IoT and predictive analytics on health and the consumer. At the end of this presentation, th...
    There is an ever-growing explosion of new devices that are connected to the Internet using “cloud” solutions. This rapid growth is creating a massive new demand for efficient access to data. And it’s not just about connecting to that data anymore. This new demand is bringing new issues and challenges and it is important for companies to scale for the coming growth. And with that scaling comes the need for greater security, gathering and data analysis, storage, connectivity and, of course, the need to find a platform (solution) that is capable of quickly and easily managing all of these comp...
    Performance is the elusive butterfly of API development. Everybody is intrigued with its beauty, yet few know how to capture it. In the old days, the approach of many shops to ensure a performant API was to create some code and then pass it over to the wall to QA to do load testing. Later some integration testing took place. As long as the API worked and it was met some marginal performance benchmarks, things were good. This worked well when a public, HTTP based API, consumed by a wide variety of distributed devices was more the exception than the rule. However, today APIs are a big deal a...
    Small teams are more effective. The general agreement is that anything from 5 to 12 is the 'right' small. But of course small teams will also have 'small' throughput - relatively speaking. So if your demand is X and the throughput of a small team is X/10, you probably need 10 teams to meet that demand. But more teams also mean more effort to coordinate and align their efforts in the same direction. So, the challenge is how to harness the power of small teams and yet orchestrate multiples of them to get higher throughput. In the context of enterprise Agile, this is very critical.
    A lot of companies believe that they can transform the way they do business, and go so far as to include transformation in some job titles. You might be an architect, an agile coach, or otherwise responsible for setting the new standards or transitioning to a new set of tools. Let me suggest two quick tips to make your life easier.
    I spend a lot of time helping organizations to “think like a data scientist.” My book “Big Data MBA: Driving Business Strategies with Data Science” has several chapters devoted to helping business leaders to embrace the power of data scientist thinking. My Big Data MBA class at the University of San Francisco School of Management focuses on teaching tomorrow’s business executives the power of analytics and data science to optimize key business processes, uncover new monetization opportunities and create a more compelling, engaging customer and channel engagement.
    Today organizations are spending millions on digital transformation initiatives - integrating advanced analytics, AI and platforms. While this is the best portfolio for them to invest on, they need to periodically step back and evaluate the end objectives. As I look back at my own experiences as a customer to my multiple 'digitally enabled' service providers I realize that there is a lot of ground to be covered in terms of meeting customer expectations even with their present digital arsenal. Application of design thinking approaches to better map customer experiences and journeys should be a ...
    SAP Ariba offers new ways for small businesses to make and manage the connections that matter to them most using cloud-based networks to bring intelligent buying and digital business benefits to any type of company. The next BriefingsDirect technology innovation thought leadership discussion examines new ways for small businesses to make and manage the connections that matter to them most using cloud-based networks to bring intelligent buying and digital business benefits to any type of company.
    Another of the main foundations of the AT&T Domain 2.0 program is the ‘DCAE’ framework: Data Collection, Analytics and Events. In short their Big Data platform for enabling smart management. “In the D2 vision, virtualized functions across various layers of functionality are expected to be instantiated in a significantly dynamic manner that requires the ability to provide real-time responses to actionable events from virtualized resources, ECOMP applications, as well as requests from customers, AT&T partners and other providers.
    Since Gartner’s latest research around bimodal IT was published, the buzzword has been catching some serious heat. This week, the fires quell as we learn best practices to balance bimodal IT for success. Another buzzword picking up traction this week is Agile. Whether it’s in development, testing or leadership, every corner of the enterprise is adopting Agile to not only accelerate delivery times and improve product quality, but to ensure effective management as well. Continue reading for the latest trends in Bimodal IT, Agile, tech leadership and IoT.
    The EMV liability shift that began in October 2015 is likely to reduce card present payment card fraud. That’s a double-edged sword for retailers with an online presence and those who accept mobile payments, as fraudsters are seeking easier routes to ill-gotten gain. Add to this the ongoing data breach environment that has become the new normal, and securing payment transactions has never been more significant.
    More is being required today of network infrastructure than ever before, due in part to the changing latency and bandwidth needs of modern applications. Wide area networks (“WANs”) are feeling the pressure, especially those that use many technologies from different services providers, are geographically diverse and are stretched to the limit by increased video and cloud app usage. In view of these challenges, hybrid WAN architectures with advanced application-level traffic routing are of particular interest. They combine the reliability of private lines for critical business applications wit...